JP2024114913A - Diagnostic system, resistance value estimation method, and computer program - Google Patents

Abstract

To provide a diagnosis system, a resistance value estimation method, and a computer program which can predict a change in resistance value of insulating resistance with good accuracy.SOLUTION: A diagnosis system comprises a measurement unit, an estimation expression storage unit, a resistance value estimation unit, and an insulating resistance information storage unit. The measurement unit measures at least one of the material factor of insulating resistance used in the target equipment and the adhering amount of an ionic pollutant of the environment factor. The estimation expression storage unit stores information regarding an estimation expression for estimating the resistance value of the insulating resistance on the basis of the result of measurement by the measurement unit. The resistance value estimation unit estimates the resistance value of the insulating resistance using the result of measurement by the measurement unit and the estimation expression stored in the estimation expression storage unit. The insulating resistance information storage unit stores one or a plurality of the result of resistance value estimation by the resistance value estimation unit, the material factor, and the environment factor.SELECTED DRAWING: Figure 1

Description

Embodiments of the present invention relate to a diagnostic system, a resistance value estimation method, and a computer program.

Electric power equipment is an important piece of equipment that supports social infrastructure, and it is expected to operate stably for a long period of time. To ensure stable operation, it is necessary to understand the deterioration state of the power equipment and to carry out its maintenance and renewal in a planned manner. The insulating properties of insulating materials used for conductor supports or barriers in power equipment deteriorate over time due to the deterioration of the material itself and the adhesion of dust or gas floating in the installation environment. If the insulating properties deteriorate, discharge or tracking may occur, which may lead to the equipment being shut down. Therefore, the condition of insulating materials serves as a barometer for diagnosing the deterioration of power equipment.

The influence of the installation environment on the deterioration of insulating materials is not limited to contamination due to the adhesion of dust and gas. In an environment where environmental factors that react chemically with the components of insulating materials are present, insulating materials may deteriorate at a rate that exceeds normal deterioration over time. For example, calcium carbonate is often used as an inorganic filler for insulating materials. When calcium carbonate reacts with chlorine gases or nitrogen oxide gases, calcium chloride or calcium nitrate is formed on the surface of the insulating material. These substances absorb moisture from the air and deliquesce even at low humidity levels of 40% RH (relative humidity) or less, so even under low humidity conditions, condensation can form on the surface of the insulating material, and leakage current can flow across the surface of the insulating material. If this becomes severe, the insulation can be destroyed, and in the worst case, it can lead to equipment shutdowns.

It is possible to directly measure the insulation resistance of insulating materials used in electrical equipment in the field. However, the measured value is greatly affected by the atmosphere of the measurement location, specifically the humidity. For example, in a dry environment, the measured insulation resistance value is often higher than the actual value, which can lead to deterioration of the insulating material being overlooked. Furthermore, electrical equipment mostly stops operating due to poor insulation during periods of high temperature and humidity, such as the rainy season. As such, directly measuring the resistance value of insulating materials is not suitable for practical use.

A method has been proposed to estimate the insulation resistance value of insulating materials through numerical calculations using methods such as multivariate analysis. In other words, this method involves measuring multiple items that are correlated with insulation resistance and are not affected by the atmosphere at the measurement location, and calculating the insulation resistance value based on the values of those items. With this method, data that correlates with insulation resistance is obtained for insulating materials used in the field and insulating materials that have been subjected to forced deterioration, and an estimation formula for insulation resistance, which is a diagnostic index, is formulated through multivariate analysis. In insulation diagnosis, the items for which the estimation formula was formulated are measured, and the insulation resistance at any temperature and humidity is estimated from the insulation resistance estimation formula.

The degradation diagnosis device according to the prior art estimates an insulation resistance value based on actual measured values of a predetermined evaluation item and a pre-stored estimation formula. The degradation diagnosis device according to the prior art also determines the validity of the estimation formula based on the estimated insulation resistance value and the actual measured resistance value. Furthermore, the degradation diagnosis device according to the prior art calculates the expiration date of an insulating material based on the estimated insulation resistance value and the period of use of the insulating material.
In the conventional technology, a DC voltage is applied to the insulating material to be diagnosed to measure the change in insulation resistance over time. The contamination state of the insulating material is diagnosed based on the constant of an exponential approximation curve obtained by approximating the relationship between the insulation resistance and the measurement time with an exponential equation. The deterioration state of the insulating material is diagnosed based on the constant of a power approximation curve obtained by approximating the relationship between the insulation resistance and the measurement time with a power equation.
In the conventional technology, a formula for estimating insulation resistance is created in advance by performing a multivariate analysis based on the relationship between the change in insulation resistance of the insulating material, which is the insulation deterioration judgment criterion, and the material properties of the insulating material and the atmospheric environmental factors in which the insulating material is installed.The insulation resistance of the insulating material at the maximum temperature and humidity assumed in the installation environment of the insulating material is calculated using the estimation formula.Then, the time until the life threshold is determined based on the calculated insulation resistance of the insulating material at the maximum temperature and humidity.

To evaluate the degree of influence of environmental factors that affect the insulation characteristics, it is necessary to measure the degree of contamination of the surface (insulating material) of equipment used in the field. Specifically, the degree of influence of contamination due to dust, sea salt particles, and gas adhesion, as well as deliquescent substances that absorb moisture at low humidity and are the result of chemical reactions between these foreign contamination factors and the components of the insulating material, is evaluated. The measurement method used for this purpose is to wipe off a certain area of the contaminated material on the surface of the target object with a damp gauze, and then measure the electrical conductivity and analyze the ion concentration of the contaminated liquid extracted with a certain amount of pure water. To carry out this measurement method, an inspector must actually perform sampling work from the target equipment. However, it is practically difficult for an inspector to perform sampling work continuously near the target equipment at all times, and there is a problem that the evaluation must be based on sampling at specified intervals.

It was also difficult to predict the deterioration of insulation resistance in advance. In particular, it was difficult to predict the deterioration of insulation resistance in advance when the conditions for the arrival of foreign contaminants changed due to the construction, renovation, or construction of surrounding structures, or abnormal weather such as typhoons, or when foreign contaminants reacted with insulating materials, causing a rapid deterioration of insulation.

Patent No. 5951299 Patent No. 5836904 Patent No. 5872643

The problem that the present invention aims to solve is to provide a diagnostic system, a resistance value estimation method, and a computer program that can accurately predict changes in the resistance value of insulation resistance.

The degradation diagnosis system of the embodiment has a measurement unit, an estimation formula storage unit, a resistance value estimation unit, and an insulation resistance information storage unit. The measurement unit measures at least one of a material factor related to the insulating material of the insulation resistor used in the target device, or an amount of ionic pollutants attached among environmental factors at the location where the insulation resistor is installed. The estimation formula storage unit stores information related to an estimation formula for estimating the resistance value of the insulation resistor based on the measurement result by the measurement unit. The resistance value estimation unit estimates the resistance value of the insulation resistor using the measurement result by the measurement unit and the estimation formula stored in the estimation formula storage unit. The insulation resistance information storage unit stores one or more of the resistance value estimation result by the resistance value estimation unit, the material factor, and the environmental factor.

1 is a block diagram showing a schematic functional configuration of a degradation diagnosis device according to a first embodiment; FIG. 11 is a diagram showing a specific example of a change trend of an estimation result of an insulation resistance value. FIG. 11 is a diagram showing a specific example of a change trend of an estimation result of an insulation resistance value. 1 is a schematic diagram showing a first arrangement example when a degradation diagnosis device is provided near a device to be diagnosed in the first embodiment; 4 is a schematic diagram showing a second arrangement example when the degradation diagnosis device is provided near the diagnosis target device in the first embodiment; FIG. FIG. 2 is a cross-sectional view showing a sensor as an example of the configuration of the environmental factor measuring unit according to the first embodiment. FIG. 2 is a plan view showing a sensor which is a configuration example of the environmental factor measuring unit of the first embodiment. FIG. 2 is a schematic diagram showing a sensor that is an example of an environmental factor measuring unit that uses the quartz crystal oscillator method according to the first embodiment. 1 is a schematic diagram showing a configuration example of an ion component analyzer for analyzing and measuring ion components in a first embodiment; FIG. 11 is a block diagram showing a schematic functional configuration of a degradation diagnosis system according to a second embodiment. FIG. 11 is a block diagram showing a schematic functional configuration of a degradation diagnosis system according to a third embodiment. FIG. 11 is a diagram showing a specific example of a change trend of a material factor. FIG. 11 is a diagram showing a specific example of a change trend of an environmental factor.

The following describes the degradation diagnosis system, resistance value estimation method, and computer program of the embodiment with reference to the drawings.

First Embodiment
The degradation diagnosis device (also called a degradation diagnosis system) according to this embodiment measures material factors and environmental factors, and estimates a resistance value based on these measured values. Furthermore, the degradation diagnosis device according to this embodiment stores the resistance value estimation results in chronological order, and estimates a future resistance value based on the time-series changes in the estimation results. The material factors are factors related to the material of the insulation resistance in electrical equipment, etc. The environmental factors are factors related to the environment in which the electrical equipment, etc. is installed (particularly the environment to which the insulating material is exposed). The degradation diagnosis device according to this embodiment is configured to automatically and continuously measure these material factors and environmental factors.

In this embodiment, of the environmental factors, the focus is on ionic pollutant components in the atmosphere in particular. In other words, the degradation diagnosis device continuously measures the amount of ionic pollutant components adhering to the equipment, and can detect signs of degradation of insulation resistance characteristics before they actually occur, even if the influence of external environmental factors suddenly changes. In addition, because the degradation diagnosis device operates automatically, there is no need to regularly dispatch inspectors to the site, which can reduce labor.

1 is a block diagram showing a schematic functional configuration of a degradation diagnosis device according to this embodiment. As shown in the figure, the degradation diagnosis device 1 includes a measurement unit 2, a resistance value estimation unit 3, a validity determination unit 4, a diagnosis unit 5, a diagnosis result display unit 6, an estimation formula storage unit 7, a model-specific material name storage unit 8, an analysis data storage unit for creating an estimation formula 11, an estimation formula creation unit 12, and an insulation resistance information storage unit 13. The measurement unit 2 also includes a material factor measurement unit 21 and an environmental factor measurement unit 22.
Each of these functional units is realized, for example, by using an electronic circuit. Each functional unit may also include internal storage means such as a semiconductor memory or a magnetic hard disk drive, as necessary. Each function may also be realized by a computer and software.

The measuring unit 2 measures various factors related to deterioration of an insulation resistance (a diagnostic target) used in a target device, etc. (an electrical product or electrical equipment). The measuring unit 2 passes data of the measurement results to the resistance value estimating unit 3. Specifically, the measurement is performed as follows.
The material factor measurement unit 21 in the measurement unit 2 measures items related to deterioration of the insulating material. Items measured by the material factor measurement unit 21 include, for example, the surface color of the insulating resistor (expressed in the color space of L*, a*, b*, for example, or a color image), blue reflectance, red reflectance, glossiness (when the incidence angle is 20 degrees), glossiness (when the incidence angle is 60 degrees), glossiness (when the incidence angle is 85 degrees), surface roughness, wettability (contact angle), and spectral reflectance spectrum. The characteristics measured by these items are characteristics that change with deterioration of the resistor material.
The environmental factor measuring section 22 in the measuring section 2 measures items related to the environment surrounding the insulation resistance. Items measured by the environmental factor measuring section 22 include, for example, the amount of ionic substances (such as the amount of adhesion), and the temperature and humidity in the installation environment. Specific examples of ionic substances include chloride ions, nitrate ions, sulfate ions, sodium ions, and ammonium ions.

At least one of the material factor measurement unit 21 and the environmental factor measurement unit 22 may be configured to perform automatic and continuous measurement. This makes it possible to accurately detect the signs of a drop in insulation resistance value due to a sudden change in circumstances (environment, etc.). When comparing material factors and environmental factors, environmental factors are more likely to cause changes in resistance characteristics due to sudden changes in circumstances. In other words, by continuously measuring environmental factors, it is possible to more effectively predict changes in resistance characteristics due to sudden changes in circumstances. However, continuous measurement itself is also effective with regard to material factors.

In other words, the measurement unit 2 measures at least one of the following: material factors related to the insulating material used in the target device, or the amount of ionic pollutants adhering to the device, which is an environmental factor at the location where the insulation resistor is installed.

The resistance value estimation unit 3 receives data of the measurement result from the measurement unit 2. The resistance value estimation unit 3 also associates the model identification information of the device to be diagnosed with the measurement result data. The model identification information may be received from the measurement unit 2, or may be stored by the resistance value estimation unit 3 as preset data, etc. The resistance value estimation unit 3 performs calculations to estimate the resistance value of the insulation resistance based on the measurement result data. The resistance value estimation unit 3 then records the resistance value of the insulation resistance obtained as the estimation result in the insulation resistance information storage unit 13 in association with the time information at which the resistance value was obtained. The associated time information is time information related to the estimation result. For example, it may be information indicating the time at which the measurement was performed by the measurement unit 2, or it may be information indicating the time at which the resistance value estimation unit 3 performed the estimation.

Specifically, the resistance value estimation unit 3 performs estimation according to the following procedure. First, the resistance value estimation unit 3 identifies the model and manufacturing date of the target device. Then, the resistance value estimation unit 3 reads the name of the insulating material corresponding to the combination of the model and manufacturing date from the model-specific material name storage unit 8. This identifies the insulating material used in the target device. Next, the resistance value estimation unit 3 reads the resistance value estimation formula for the identified insulating material from the estimation formula storage unit 7. Next, the resistance value estimation unit 3 applies the measurement value data received from the measurement unit 2 to the estimation formula read from the estimation formula storage unit 7. Then, the resistance value estimation unit 3 calculates the resistance value (estimated value) of the insulation resistance of the target device by performing calculations using the estimation formula. In other words, the resistance value estimation unit 3 calls up the insulation resistance estimation formula of the identified insulating material from the estimation formula storage unit 7 by referring to the model-specific material name storage unit 8, and estimates the insulation resistance value of the insulating material using this estimation formula.

In other words, the resistance value estimation unit 3 estimates the resistance value of the insulation resistance using the measurement results from the measurement unit 2 and the estimation formula stored in the estimation formula storage unit 7. The resistance value estimation unit 3 then records the resistance value of the insulation resistance obtained as the estimation result in the insulation resistance information storage unit 13 in association with the time information at which the resistance value was obtained.

The insulation resistance information storage unit 13 is configured using a storage device such as a magnetic hard disk drive or a semiconductor storage device. The insulation resistance information storage unit 13 stores time-series information regarding the insulation resistance that is the subject of measurement by the measurement unit 2. For example, the insulation resistance information storage unit 13 may store time-series information on the results of the resistance value estimation by the resistance value estimation unit 3.

The validity determination unit 4 determines the validity of the insulation resistance value estimated by the resistance value estimation unit 3. The validity determination unit 4 determines the validity of the estimated insulation resistance value based on, for example, a comparison with a predetermined upper limit value for the resistance value. Alternatively, the validity determination unit 4 may determine the validity of the estimated insulation resistance value based on, for example, a comparison with an actual measured value of the insulation resistance value.
In other words, the validity determining unit 4 can determine the validity of the estimation formula by comparing the resistance value estimated by the resistance value estimating unit 3 with the actual measured resistance value of the insulation resistor.

The diagnostic unit 5 performs diagnostic processing based on the time series changes in the insulation resistance value estimated by the resistance value estimation unit 3. Specific examples of diagnostic processing include processing for determining a trend of change, processing for estimating a future insulation resistance value, processing for estimating the remaining life of the insulation resistance, and processing for detecting the occurrence of an abnormality. The diagnostic unit 5 may perform one or more of the processes described below as specific examples of diagnostic processing. Each specific example of diagnostic processing is described below.

1) Process for Determining Change Trend The diagnostic unit 5 determines the change trend of the insulation resistance value over time. Any method may be used for the diagnostic unit 5 to determine the change trend. For example, an existing approximation method may be used. More specifically, the process is as follows. The diagnostic unit 5 may determine the change trend of the insulation resistance value over time by using any of predetermined approximation methods, such as linear approximation, logarithmic approximation, exponential approximation, and power approximation. FIG. 2 is a diagram showing a specific example of the change trend. Time A indicates the current time. Therefore, up to time A, an estimated value of the insulation resistance value is obtained based on the measurement results actually obtained by the measurement unit 2. On the other hand, since the time after time A is in the future, no measurement results have been obtained by the measurement unit 2. Therefore, an estimated value of the insulation resistance value based on the measurement results (estimated result by the resistance value estimation unit 3) has not been obtained. A plurality of circles located up to time A indicate estimated values of the insulation resistance value at each time. By approximating these multiple estimated values, a solid approximation line extending to time A is obtained. Such an approximation line (a straight line in FIG. 2) indicates the change trend of the insulation resistance value over time.

The diagnostic unit 5 may dynamically determine which approximation method to use, for example, from linear approximation, logarithmic approximation, exponential approximation, power approximation, etc., and may determine the change trend of the insulation resistance value over time by using the determined approximation method. For example, the diagnostic unit 5 may have multiple approximation method candidates in advance, and may determine the change trend by adopting the approximation method that is most similar to the change trend of the time series stored in the insulation resistance information storage unit 13 from among the change trends obtained by the multiple approximation methods.

The diagnosis unit 5 may determine an approximation method suitable for the time series values of the insulation resistance value that is the subject of the current diagnosis, based on the results of machine learning performed by using multiple pieces of training data that correspond, for example, to time series values of insulation resistance values measured in the past and approximation methods suitable for those time series values.

The diagnostic unit 5 may, for example, determine the time point at which the trend of change in the time series of the insulation resistance value changes (hereinafter referred to as the "time point of change"), and when determining the trend of change after the time point of change, may determine the trend of change based only on the insulation resistance value after the time point of change without using the insulation resistance value up to the time point of change. FIG. 3 is a diagram showing a specific example of the trend of change. The trend of change changes before and after time C in FIG. 3. Such a time point of change may be determined, for example, by calculating the rate of change (e.g., differential value) of the estimated value of the insulation resistance at each time. When the value of the rate of change changes suddenly beyond a threshold value, or when the value of the rate of change changes suddenly so as to become discontinuous (for example, when the difference between the rates of change at consecutive times exceeds a threshold value), that time point may be determined as the time point of change. The diagnostic unit 5 determines the trend of change after time C, which is the time point of change, without using the estimated value before time C. For example, the diagnostic unit 5 may determine the trend of change after time C based only on the estimated value between time C and time D. In this case, time D is the time at which the estimated value of the insulation resistance based on the actual measurement result is obtained. For example, the current time. By determining the change trend in this way, it is possible to determine the change trend with greater accuracy. As a result, it becomes possible to estimate each value with greater accuracy in the process of estimating future insulation resistance values and remaining life, as described below.

2) Process of estimating future insulation resistance value The diagnosis unit 5 estimates a future insulation resistance value based on the above-mentioned determination result of the change trend. The diagnosis unit 5 may estimate, for example, an insulation resistance value at an arbitrary time point in the future based on the determination result of the change trend. The arbitrary time point in the future may be specified by a user via an input device (not shown), or may be specified in data received from another information processing device or the like.

3) Process for estimating remaining life of insulation resistance The diagnostic unit 5 estimates the remaining life of the insulation resistance based on the above-mentioned determination result of the change trend. Specifically, the process is as follows. The diagnostic unit 5 prestores a lower limit value of the insulation resistance value. The lower limit value is a value indicating that if the insulation resistance value falls below this value, it will not satisfy a predetermined standard and maintenance such as replacing the insulating material will be required. The diagnostic unit 5 determines the timing at which the estimated value of the insulation resistance falls below a threshold value indicating the lower limit value. The diagnostic unit 5 estimates the remaining period until such timing as the value of the remaining life. For example, in the case of FIG. 2, if the insulation resistance value deteriorates according to the change trend, the estimated value falls below the threshold value at time B. Therefore, the period from the current time (time A) to time B is determined as the remaining life. For example, in the case of FIG. 3, if the insulation resistance value deteriorates according to the change trend, the estimated value falls below the threshold value at time E. Therefore, the period from the current time (time D) to time E is determined as the remaining life.

4) Processing for detecting occurrence of abnormality The diagnostic unit 5 may determine whether the above-mentioned change time point exists, and may detect the occurrence of an abnormality when it is determined that the change time point exists. When the diagnostic unit 5 determines that the change time point exists, information on the change time point may be output to the user. Such output may be performed, for example, by the diagnostic result display unit 6. A sudden change in the insulation resistance value may be caused, for example, by an increase in load current or a sudden change in the environment. When such an event occurs, an abnormality such as damage to the protective device or damage to the surrounding protective housing may occur. Therefore, by detecting the occurrence of such an abnormality and outputting it to the user, it is possible for the user to take some kind of action at an earlier point in time.

The diagnosis result obtained by the processing of the diagnosis unit 5 is passed to the diagnosis result display unit 6 .
The diagnostic result display unit 6 displays the diagnostic result passed from the diagnostic unit 5. This allows the user of the degradation diagnosis device 1 to know the estimated expiration date of the resistor used in the target device. The diagnostic result display unit 6 may be replaced with a device that outputs in another manner. For example, it may be replaced with a speaker that outputs information by voice, or it may be replaced with one or more lights that output information by flashing or blinking.

The estimation formula storage unit 7 is configured using a storage device such as a magnetic hard disk drive or a semiconductor storage device. The estimation formula storage unit 7 stores an estimation formula for each insulating material. The estimation formulas stored in the estimation formula storage unit 7 cover a plurality of deterioration modes and are created in advance by the T-method based on analysis data. These estimation formulas can be generalized and expressed by the following formula (1).
R=f(m, d ₁ , d ₂ ,..., d _N )... (1)
In formula (1), R is the estimated resistance value, and f() is a predetermined function for outputting the estimated resistance value. Furthermore, m is a value (e.g., an integer value) that indicates the type (substance) of the insulating material. Furthermore, _d1 , _d2 , ..., _dN are N parameters that indicate things other than the type of insulating material. These parameters include data on the shape and size (e.g., cross-sectional area, length, etc.) of the insulating material. Furthermore, these parameters include measurement result data measured by the measurement unit 2.
In addition, the more degradation modes that are covered when creating the estimation equation, the higher the accuracy of the estimated resistance value will be, and the fewer misdiagnoses will occur.

The estimation formula storage unit 7 stores this function f(). Specifically, the estimation formula storage unit 7 may store, for example, a formula representing the function f() as data in the form of a syntax tree. The estimation formula storage unit 7 may also store, for example, a computer program module (such as a source program or an executable program) that realizes the function f(). Instead of storing the function f(), the estimation formula storage unit 7 may also store a function f _m () that is individually defined for each type m of insulating material (for example, m = 1, 2, ...). The function f _m () is expressed by the following formula (2).
R=f _m (d ₁ , d ₂ ,..., d _N )... (2)

In other words, the estimation formula storage unit 7 stores information about the estimation formula for estimating the resistance value of the insulation resistance based on the measurement results by the measurement unit 2.

The model-specific material name storage unit 8 is configured using a storage device such as a magnetic hard disk drive or a semiconductor storage device. The model-specific material name storage unit 8 stores the model and manufacturing date of the device to be diagnosed in association with the type of insulating material. In other words, by identifying the model and manufacturing date of the target device and referring to the model-specific material name storage unit 8, it is possible to identify the type of insulating material used.

The analytical data storage unit 11 for creating an estimation formula is configured using a storage device such as a magnetic hard disk device or a semiconductor storage device. The analytical data storage unit 11 for creating an estimation formula stores analytical data used to create an estimation formula. The data stored in the analytical data storage unit 11 for creating an estimation formula is a set of values of m, d ₁ , d ₂ , ..., d _N , and R in formula (1) and formula (2).
The estimation formula creation unit 12 performs multivariate analysis by the Taguchi method (T method) based on the analysis data stored in the estimation formula creation analysis data storage unit 11 to obtain an estimation formula. The estimation formula obtained by the estimation formula creation unit 12 is expressed by the above-mentioned formula (1) or formula (2). The estimation formula creation unit 12 may use the Mahalanobis-Taguchi method (MT method) instead of the T method. The T method and the MT method themselves are existing technologies.
In other words, the estimation equation creation unit 12 creates an estimation equation in advance by multivariate analysis using the Taguchi method or the Taguchi-Schmidt method, based on a pair of actual measured values of the measurement items measured by the measurement unit 2 and the actual measured resistance value of the insulation resistance.

The degradation diagnosis device 1 may be configured not to have the estimation equation creation analysis data storage unit 11 or the estimation equation creation unit 12. Even in this case, the estimation equation estimated by the estimation equation creation unit 12 of a device other than the degradation diagnosis device 1 is stored in advance in the estimation equation storage unit 7. Therefore, the resistance value estimation unit 3 can estimate the resistance value using the estimation equation.

4 is a schematic diagram showing an example of arrangement (first arrangement example) in the case where a deterioration diagnosis device according to this embodiment is provided near a diagnosis target device. As shown in the figure, in this example, target devices 312 and 313 are installed inside a housing 311. The target devices 312 and 313 are electrical devices that use high voltage and have an insulating material inside for insulation. The housing 311 has an air intake 316 and an exhaust 317. For example, a fan (not shown) is provided at a predetermined location inside or near the housing 311, so that air outside the housing 311 flows into the inside of the housing 311 through the air intake 316. Similarly, air inside the housing 311 flows out to the outside of the housing 311 through the exhaust 317. The thick arrows shown at the positions of the air intake 316 and the exhaust 317 respectively indicate the direction of air flow. In this example, air is exchanged between the inside and outside of the housing 311 basically only through the intake port 316 and the exhaust port 317. Measurement units 314 and 315 are installed near the target devices 312 and 313, respectively. These measurement units 314 and 315 measure the polluting substances at the locations. That is, the measurement unit 314 measures the environmental factors that affect the target device 312. Also, the measurement unit 315 measures the environmental factors that affect the target device 313. That is, the measurement units 314 and 315 correspond to the environmental factor measurement unit 22 in FIG. 1. Note that a measurement unit (not shown) for measuring material factors (corresponding to the material factor measurement unit 21 in FIG. 1) may be provided for each of the target devices 312 and 313. In the example shown in FIG. 4, the measurement units 314 and 315 are connected to the outside of the housing 311 via a communication line. Functions of the degradation diagnosis device 1 other than the measurement unit 2 (i.e., functions including the resistance value estimation unit 3, the validity determination unit 4, the diagnosis unit 5, the diagnosis result display unit 6, the estimation formula storage unit 7, the model-specific material name storage unit 8, the analysis data storage unit 11 for creating the estimation formula, and the estimation formula creation unit 12) exist outside the housing 311. Each of the measurement units 314 and 315 transmits the measurement result data to the resistance value estimation unit 3 via the above-mentioned communication line.

4, airborne contaminants (ionic substances, etc.) are distributed within the housing 311 according to the airflow within the housing 311. By providing a measuring unit in the vicinity of each target device, it is possible to measure the contamination state (environmental factors) of each target device.
That is, the measuring means for measuring the amount of adhesion of ionic pollutants in the measuring unit 2 is arranged in the vicinity of the insulation resistance of the target device, thereby making it possible to more accurately measure the influence of the environment on the insulation resistance.

In addition, when the air flow within the housing 311 is clear, it is possible to measure only the areas that are most likely to be soiled as the monitored objects. For example, in the arrangement shown in FIG. 4, only the measuring unit 314 corresponding to the target device 312 located upstream of the air flow (roughly indicated by the thick arrow) may be provided.

Figure 5 is a schematic diagram showing a second arrangement example when a deterioration diagnosis device according to this embodiment is provided near the equipment to be diagnosed. In the example shown in this figure, the target equipment 312 and 313 are installed inside the housing 311, as in the case shown in Figure 4. The housing 311 also has an intake port 316 and an exhaust port 317. That is, as in the case shown in Figure 4, the air outside the housing 311 flows into the inside of the housing 311 through the intake port 316. The air inside the housing 311 also flows out to the outside of the housing 311 through the exhaust port 317. A measuring unit 318 is installed near the intake port 316 inside the housing 311. The measuring unit 318 measures the polluting substances at that location. The air flowing into the housing 311 from the intake port 316 passes around the measuring unit 318, circulates inside the housing 311, and flows out of the housing 311 through the exhaust port 317. That is, the measuring unit 318 measures environmental factors that affect the target devices 312 and 313. That is, the measuring unit 318 corresponds to the environmental factor measuring unit 22 in FIG. 1. Note that a measuring unit (not shown) for measuring material factors (corresponding to the material factor measuring unit 21 in FIG. 1) may be provided for each of the target devices 312 and 313. In the example shown in FIG. 5, the measuring unit 318 is also connected to the outside of the housing 311 via a communication line. Functions of the degradation diagnosis device 1 other than the measurement unit 2 (i.e., functions including the resistance value estimation unit 3, the validity determination unit 4, the diagnosis unit 5, the diagnosis result display unit 6, the estimation formula storage unit 7, the model-specific material name storage unit 8, the analysis data storage unit 11 for creating the estimation formula, and the estimation formula creation unit 12) exist outside the housing 311. The measurement unit 318 transmits the measurement result data to the resistance value estimation unit 3 via the above-mentioned communication line.

5, a measuring unit 318 provided near the intake 316 measures the amount of pollutants (ionic substances, etc.) flowing into the housing 311. It is estimated that the amount of pollutants will be less in the locations of the target devices 312 and 313 that are the actual monitoring targets than in the vicinity of the intake 316. In other words, the illustrated arrangement is effective in detecting signs of a decrease in the insulation resistance value of the target devices 312 and 313 in advance.
When a sign of a decrease in insulation resistance is detected, manual visual inspection or conventional measurement of the degree of contamination can be performed for each target device, making it possible to achieve preventive maintenance while keeping the number of installed measuring devices to a minimum.

That is, in this example, the means for measuring the amount of attached ionic pollutants in the measuring section 2 is arranged in the vicinity of the air intake of the housing that houses the insulation resistor in the target device.
Furthermore, the above-mentioned measuring means for measuring the amount of attached ionic pollutants may also be installed near the exhaust port of the housing.

Next, an example of the implementation of the environmental factor measuring unit 22 will be described.
6 and 7 are a cross-sectional view and a plan view showing a sensor which is one configuration example of the environmental factor measuring unit 22. Fig. 6 is a cross-sectional view. Fig. 7 is a plan view. The cross-sectional view of Fig. 6 shows a cross section taken along dashed line CC' in Fig. 7.
As shown in Fig. 6 and Fig. 7, the environmental factor measuring unit 22 according to this embodiment has an insulating plate 31, a first electrode 32, and a second electrode 33. The first electrode 32 and the second electrode 33 are arranged in a predetermined pattern on the insulating plate 31 at a predetermined distance from each other. The pattern of the first electrode 32 and the second electrode 33 is formed, for example, by a printing technique. As shown in Fig. 7, the first electrode 32 and the second electrode 33 are respectively connected to a voltage source ("V" in the figure) and an ammeter ("A" in the figure). The voltage source applies an AC voltage or a DC voltage between the first electrode 32 and the second electrode 33. The ammeter measures the current flowing from the first electrode 32 to the second electrode 33. In Fig. 6, the reference numeral 34 denotes a contaminant or a water film. Due to the adhesion of the contaminant/water film 34, a leakage current flows from the first electrode 32 to the second electrode 33. The above-mentioned ammeter measures this leakage current. That is, the ammeter measures the ionic conductivity of the contaminants/water film 34 attached to the electrode pattern (first electrode 32 and second electrode 33). The measured current varies depending on the amount of attached contaminants or water film.

As described above, in this example, the means for measuring the amount of ionic pollutants attached in the measurement unit 2 measures the electrical conductivity of a water film containing ionic pollutants attached to a sensor having electrodes.

Alternatively, dissimilar metals may be used for the first electrode 32 and the second electrode 33, and the galvanic current flowing between the electrodes may be measured. In this case, the galvanic current changes depending on the amount of attached moisture and the amount of attached ionic contaminants. In other words, by measuring the galvanic current, it is possible to estimate the amount of attached ionic contaminants between the electrodes.

The temperature and humidity at the time of measurement affect the amount of moisture between the electrodes. In other words, the measurement value by the ammeter also changes depending on the temperature and humidity. For this reason, the correlation between the degree of contamination of the attached matter, the temperature, the humidity, and the sensor measurement value (current) is acquired in advance. This makes it possible to correct the contribution of moisture from the measured current value. The environmental factor measurement unit 22 then measures the temperature, humidity, and current, and estimates the degree of contamination of the attached matter from these measurement values. As described above, the environmental factor measurement unit 22 measures the degree of attachment of the contaminants that are factors that reduce the insulation resistance value.
In this case, the estimation formula calculates an estimated resistance value based on temperature and humidity as well.

Other examples of implementing the environmental factor measuring unit 22 will also be described.
In order to capture the mass change due to the adhesion of contaminants, the quartz crystal microbalance (QCM) method or the surface acoustic wave (SAW) method can be applied. In this way, the method of determining the amount of adhesion of contaminants by measuring the change in vibration characteristics accompanying a minute change in weight is relatively easy and can detect and quantify the amount of adhesion of contaminants with high sensitivity.

Figure 8 is a schematic diagram showing a sensor that is an example of the configuration of the environmental factor measurement unit 22 using the quartz crystal oscillator method. As shown in the figure, the sensor of this example is configured by sandwiching a quartz crystal oscillator 40 between electrodes 43 and 44. A voltage can be applied between the electrodes 43 and 44 from the outside. When a voltage is applied between these electrodes, the quartz crystal oscillator 40 vibrates at a specific frequency (resonant frequency). In addition, the deposits 41 and 42 are contaminants. The mass changes when the deposits 41 and 42 are attached and when they are not attached, so the resonant frequency of the quartz crystal oscillator 40 changes. In addition, the resonant frequency of the quartz crystal oscillator 40 changes depending on the amount of the deposits. In the sensor of this example, the mass of the deposits can be measured based on the change in the resonant frequency. It is possible to measure a mass change on the order of 0.1 ng (nanogram) as a measurement accuracy.

In other words, the means for measuring the amount of ionic pollutants attached in the measurement unit 2 measures the change in the mass of the sensor due to the attachment of pollutants, including ionic pollutants, to the sensor.

As with the configuration examples shown in Figures 6 and 7, in the configuration example of Figure 8, the temperature and humidity at the time of measurement affect the amount of moisture adhering to the quartz oscillator. In other words, the measured value of the mass change changes depending on the temperature and humidity. For this reason, the correlation between the degree of contamination of the attached matter, the temperature, humidity, and the sensor measurement value (current) is obtained in advance. This makes it possible to correct the contribution of moisture from the measured value of the mass change. The environmental factor measurement unit 22 then measures the temperature, humidity, and mass change, and estimates the degree of contamination of the attached matter from these measured values.

Ion components contained in the adhering pollutants are measured using methods such as ion chromatography and ion-selective electrodes. To use these methods, the ionic components of the pollutants that have arrived must be dispersed in water, which must then be supplied to an analytical device.

Figure 9 is a schematic diagram showing an example of the configuration of an ion component analyzer for analyzing and measuring ion components. As shown in the figure, the ion component analyzer 50 includes a pure water supply unit 51, a pure water supply pipe 52, a contaminant collection tank 53, a sampling pipe 54, a three-way valve 56, a cleaning drain pipe 57, and a component measurement unit 58. This ion component analyzer 50 functions as follows.

The pure water supply unit 51 supplies pure water through a pure water supply pipe 52 when necessary.
The pure water supply pipe 52 supplies the pure water supplied from the pure water supply unit 51 to the contaminant sampling tank 53 when necessary.
The contaminant sampling tank 53 samples contaminants that have come flying in from the outside. While the ion component analyzer 50 is left unused, contaminants from the outside accumulate in the contaminant sampling tank 53. After the contaminants accumulate during the left-behind period, ionic substances contained in the contaminants dissolve in the pure water supplied from the pure water supply pipe 52 during the dissolution period. The water in which the ionic substances have been dissolved flows from the contaminant sampling tank 53 to the sampling pipe 54.
The sampling pipe 54 has the function of supplying the water containing ionic substances collected in the contaminant sampling tank 53 to a component measuring section 58 via a three-way valve 56 .
A pump is provided between the pollutant sampling tank 53 and the three-way valve 56. This pump generates water pressure in the sampling pipe 54. The action of this pump causes water to flow in the sampling pipe 54 from the left side to the right side in the figure.
The three-way valve 56 is a valve having three-way inlets and outlets. In the ion component analyzer 50, the three-way valve 56 can switch between directing the water from the soil sampling tank 53 to the component measuring unit 58 side or to the cleaning drain pipe 57 side. In other words, when the component measuring unit 58 is performing a measurement, the water from the soil sampling tank 53 is supplied to the component measuring unit 58 side. When the measurement by the component measuring unit 58 is completed, the water from the soil sampling tank 53 flows to the cleaning drain pipe 57 side and is drained.
The cleaning drain pipe 57 is a pipe for draining unnecessary water (water stored in the soil sampling tank 53).
The component measuring unit 58 measures the types and amounts of ion components contained in the supplied water by ion chromatography, a method using an ion selective electrode, etc. The component measuring unit 58 outputs data on the measurement results.

By using the ion component analyzer 50 configured as described above, the following process is considered as one cycle: (1) accumulation of contaminants from the outside → (2) analysis and measurement of ion components → (3) drainage of water not used in measuring the ion components. Measurements can be repeated. The measurement cycle can be set appropriately.

In other words, the measuring means for measuring the amount of ionic pollutants in the measuring unit 2 includes an ion component analyzer 50 for analyzing the composition of the ionic pollutants. The ion component analyzer 50 analyzes the composition of the ionic pollutants qualitatively and quantitatively.

As described above, according to this embodiment, the resistance value can be accurately predicted by having a measurement unit that measures at least one of the material factors related to the insulating material of the insulation resistor used in the target device or the amount of ionic pollutants attached among the environmental factors at the location where the insulation resistor is installed, and a resistance value estimation unit that estimates the resistance value of the insulation resistor using the measurement results.

In addition, depending on the structure of the electrical equipment, the degree of contamination may vary depending on the location within the equipment due to the influence of air currents.
In this embodiment, a means for measuring the amount of ionic contamination components attached may be installed in the vicinity of the insulating material to be diagnosed, which makes it possible to grasp the contamination state of the target material more accurately.

Furthermore, if a measuring device for the amount of ionic contaminant components adhering to each insulating material being diagnosed could be installed, it would be possible to predict the insulation degradation of each material with high accuracy; however, it is difficult to install measuring devices individually in all locations where insulating materials are used.
In this embodiment, a method of measuring the state of contamination at a portion of an insulating material where contamination is most likely to progress due to airflow can also be used.
A means for measuring the amount of ionic contamination components attached can be installed near the air intake of the housing that contains the insulating material to be diagnosed. In this case, although it is not possible to obtain information on the local contamination state inside the housing, it is possible to grasp the overall contamination level inside the housing.
If an increase in the overall level of contamination inside the housing is detected, preventive maintenance can be achieved while keeping the number of installed measuring means to a minimum by performing a manual visual inspection or the like.
In addition, a measuring means for measuring the amount of ionic contaminant adhesion may be installed not only at the intake port of the housing in which the insulating material to be diagnosed is housed, but also near the exhaust port of the same housing. In this case, the amount of ionic contaminant discharged from the housing can be determined from the results of the measuring means near the exhaust port. Therefore, it is possible to more accurately determine the amount of ionic contaminant remaining inside the housing.

In addition, in this embodiment, a means for measuring the ionic conductivity in the water film in which the ionic contaminants have been dissolved may be provided in order to measure the amount of adhesion of the ionic contaminants. The ionic contaminants increase the conductivity of the water film on the surface of the insulating material, thereby causing a decrease in insulation resistance. Therefore, by measuring the conductivity, it is possible to directly evaluate the degree of influence on insulation deterioration.

To measure the amount of ionic pollutants adhering, a means for measuring the change in mass due to the adhesion of pollutants can also be provided. As mentioned above, it is sea salt particles, gases, etc. that cause a decrease in insulation resistance, and as the amount of these ionic pollutants adhering increases, the mass also increases. When measuring the mass, non-ionic dust is also measured at the same time, but non-ionic dust is carried by the air current just like ionic pollutants. Therefore, when ionic pollutants increase, there is a tendency for non-ionic dust to increase at the same time. Therefore, by measuring the change in mass, it is possible to predict the increase in ionic pollutants.

Furthermore, by providing a means to qualitatively and quantitatively analyze the composition of ionic contaminant components, it will be possible to evaluate the degree of impact on insulation degradation with even greater precision.

In addition, nitrate ions are an ionic component that has a significant effect on insulation properties. When insulation resistance decreases and discharge begins to occur, nitric acid is produced using nitrogen in the air as a raw material. This nitric acid dissolves in the water film and reacts with calcium carbonate, which is the insulating material filler, producing calcium nitrate, which deliquesces at low humidity, causing a rapid deterioration of insulation. Therefore, detecting the presence of nitrate ions is an effective means of predicting the tendency for insulation to deteriorate.

As mentioned above, the influence of foreign contaminants may be dominant until a certain point, at which point the influence of nitric acid generated by discharge may become the main cause of insulation deterioration. In that case, the prediction formula for the progress of insulation resistance deterioration may change.
Therefore, by detecting an increase in the amount of nitrate ions, it is possible to grasp the change in the deterioration mode as described above and switch the prediction formula for the progress of insulation resistance degradation, enabling more accurate remaining life diagnosis.

Second Embodiment
Next, a second embodiment will be described. Note that the description of the matters already described in the previous embodiment may be omitted below. Here, the description will focus on matters unique to this embodiment.

FIG. 10 is a block diagram showing a schematic functional configuration of the degradation diagnosis system according to this embodiment.
As shown in the figure, the degradation diagnosis system 100 includes a measuring device 110 and an analyzing device 120. The measuring device 110 and the analyzing device 120 are connected to each other via a communication network. Note that a plurality of measuring devices 110 may be included in the degradation diagnosis system 100.

The measuring device 110 includes a measuring section 2 and a measurement value transmitting section 111 .
The analysis device 120 includes a measurement value receiving unit 121, a resistance value estimating unit 3, a validity determining unit 4, a diagnosis unit 5, a diagnosis result display unit 6, an estimation formula storage unit 7, a model-specific material name storage unit 8, an analysis data for creating an estimation formula storage unit 11, and an estimation formula creating unit 12. The measurement device 110 is realized, for example, by using a server-type computer.
Of the above functional units, the measurement unit 2, resistance value estimation unit 3, validity determination unit 4, diagnosis unit 5, diagnosis result display unit 6, estimation formula storage unit 7, model-specific material name storage unit 8, analysis data for creating estimation formula storage unit 11, and estimation formula creation unit 12 have the same functions as those in the first embodiment (see FIG. 1), and therefore detailed description of each of these functional units will be omitted here.

A measurement value transmitting section 111 on the measuring device 110 side transmits data obtained as a result of measurement by the measuring section 2 to the analyzing device 120 side via the communication network.
The measurement value receiving section 121 on the analytical device 120 side receives the measurement result data transmitted from the measurement value transmitting section 111 on the measuring device 110 side. The measurement value receiving section 121 passes the received measurement result data to the resistance value estimating section 3.
As a result, the analysis device 120 estimates a resistance value based on the data measured by the measurement device 110, determines the validity of the estimated resistance value, performs a diagnosis, and displays the diagnosis results.

According to this embodiment, the degradation diagnosis system 100 can be configured with a single or a small number of analysis devices 120 and a large number of measurement devices 110 in a so-called client-server configuration. In other words, the necessary measurements are performed at the site where the target equipment is installed, and the process of estimating resistance values using the data from these measurement results can be performed centrally on a small number of server devices installed in, for example, a data center. This allows the overall cost of the degradation diagnosis system 100 to be reduced.

Third Embodiment
Next, a third embodiment will be described. Note that the description of the matters already described in the previous embodiments may be omitted below. Here, the description will focus on matters unique to this embodiment.

FIG. 11 is a block diagram showing a schematic functional configuration of the degradation diagnosis system according to this embodiment.
As shown in the figure, the degradation diagnosis system 150 includes a measuring device 110 and an analyzing device 160. The measuring device 110 and the analyzing device 160 are connected to each other via a communication network. Note that a plurality of measuring devices 110 may be included in the degradation diagnosis system 100.

The measuring device 110 includes a measuring section 2 and a measurement value transmitting section 111 .
The analytical device 160 includes a measurement value receiving unit 161, an analysis data collecting unit 162, a resistance value estimating unit 3, a validity determining unit 4, a diagnosing unit 5, a diagnosis result display unit 6, an estimation formula storing unit 7, a model-specific material name storing unit 8, an analysis data for creating an estimation formula storing unit 11, and an estimation formula creating unit 12. The analytical device 160 is realized, for example, by using a server-type computer.
Of the above functional units, the measurement unit 2, resistance value estimation unit 3, validity determination unit 4, diagnosis unit 5, diagnosis result display unit 6, estimation formula storage unit 7, model-specific material name storage unit 8, analysis data for creating estimation formula storage unit 11, and estimation formula creation unit 12 have the same functions as those in the first embodiment (see FIG. 1), and therefore detailed description of each of these functional units will be omitted here.

A measurement value transmitting section 111 on the measuring device 110 side transmits data obtained as a result of measurement by the measuring section 2 to the analyzing device 120 side via the communication network.
The measurement value receiving section 161 on the analytical device 160 side receives the measurement result data transmitted from the measurement value transmitting section 111 on the measuring device 110 side. The measurement value receiving section 161 passes the received measurement result data to the resistance value estimating section 3.
As a result, the analysis device 160 estimates a resistance value based on the data measured by the measurement device 110, determines the validity of the estimated resistance value, performs a diagnosis, and displays the diagnosis results.

The measurement value receiving unit 161 on the analysis device 160 side passes the received data to the analysis data collecting unit 162. The analysis data collecting unit 162 writes the data received from the measurement value receiving unit 161 to the analysis data storage unit 11 for creating an estimation formula. That is, when the estimation formula creating unit 12 creates an estimation formula by referring to the analysis data storage unit 11 for creating an estimation formula, it performs an analysis including the data collected by the analysis data collecting unit 162. That is, the analysis device 160 can create an estimation formula using not only the analysis data stored in advance, but also the data received from the measurement device 110. Note that the analysis data used to create the estimation formula may include the actual measured resistance value.

According to this embodiment, the analysis data collection unit 162 of the analysis device 160 writes the measurement result data received from the measurement device 110 to the analysis data storage unit 11 for creating an estimation equation. That is, the analysis device 160 can create an estimation equation for estimating a resistance value using the measurement result data received from the measurement device 110. The analysis device 160 can create this estimation equation at any appropriate timing. That is, the analysis device 160 can learn about the estimation equation for resistance value using the measurement result data collected from the measurement device 110.

A modification of the embodiment will be described.
4 and 5 of the above embodiment, functions other than the measurement unit 2 of the degradation diagnosis device 1 (i.e., functions including the resistance value estimation unit 3, the validity determination unit 4, the diagnosis unit 5, the diagnosis result display unit 6, the estimation formula storage unit 7, the model-specific material name storage unit 8, the analysis data storage unit 11 for creating the estimation formula, and the estimation formula creation unit 12) exist outside the housing 311. However, the resistance value estimation unit 3, the validity determination unit 4, the diagnosis unit 5, the diagnosis result display unit 6, the estimation formula storage unit 7, the model-specific material name storage unit 8, the analysis data storage unit 11 for creating the estimation formula, and the estimation formula creation unit 12 may also exist inside the housing 311.
Furthermore, the arrangement of the measuring means for measuring the amount of attached ionic pollutants in the measuring section 2 is not limited to the examples shown in Figs. 4 and 5, and other arrangements may be used.

As a specific example of a means for measuring the amount of ionic pollutants attached, a sensor that is a method for achieving this has been described with reference to Figures 6, 7, 8, and 9, but multiple sensors may also be used in combination to measure the amount of ionic pollutants attached.

The ion component analyzer 50 shown in FIG. 9 analyzes the composition of ionic pollutants, but the resistance value estimation unit 3 may estimate the resistance value using a different estimation formula depending on the composition determined here. In other words, in this case, the resistance value estimation unit 3 switches between insulation resistance estimation formulas depending on the composition of the ionic pollutant components. This allows the resistance value estimation unit 3 to use an estimation formula that matches the composition of the ionic pollutant. In other words, it becomes possible to estimate the resistance value with greater accuracy.

When determining the time of change, the determination may be made based on whether maintenance has been performed on the insulation resistance. When maintenance is performed, environmental factors often change suddenly in a positive direction. In that case, the estimated insulation resistance based on the measured value before maintenance and the estimated insulation resistance based on the measured value after maintenance may change discontinuously at the time of maintenance. In such a case, if an approximation is performed using the estimated values before and after maintenance, there is a risk that an inaccurate approximation will be performed. To address this problem, a more accurate change trend can be obtained by treating the time of maintenance as the above-mentioned time of change. For example, the degradation diagnosis device 1 may be configured to make it possible to input the time when maintenance was performed. In this case, the insulation resistance information storage unit 13 of the degradation diagnosis device 1 may store information indicating the date and time when maintenance was performed.

The above-mentioned four diagnostic processes in the diagnosis unit 5 are all performed based on the estimation results by the resistance value estimation unit 3. However, the diagnosis unit 5 may perform diagnostic processes based on values other than the estimation results. For example, the diagnosis process may be performed based on values measured by the measurement unit 2 (hereinafter referred to as "estimation factors"). The estimation factors include either one or both of material factors and environmental factors. In this case, the resistance value estimation unit 3 records the values of the estimation factors used to estimate the resistance value in the insulation resistance information storage unit 13 in association with time information and the estimated resistance value. The diagnosis unit 5 may perform a process of determining the change trend of the estimation factors, a process of estimating future values of the estimation factors, and a process of detecting the occurrence of an abnormality according to the time series changes in the estimation factors stored in the insulation resistance information storage unit 13. All of these processes are performed by using the estimation factors instead of the estimated insulation resistance value in the above-mentioned processes 1), 2), and 4).

Furthermore, an estimate of the future insulation resistance may be obtained based on the future estimation factor obtained by the processing of the diagnosis unit 5. Specifically, the resistance value estimation unit 3 may obtain an estimate of the future insulation resistance by performing an estimation process of the insulation resistance value using the future estimation factor obtained by the processing of the diagnosis unit 5, rather than the value measured by the measurement unit 2 (measured estimation factor). Furthermore, the diagnosis unit 5 may determine the remaining life of the insulating material based on the estimate of the future insulation resistance obtained in this manner.

Figure 12 shows a specific example of the change trend of the glossiness value included in the material factor, as an example of an estimation factor. Time F indicates the current time. Therefore, up to time F, the glossiness is actually the measurement result obtained by the measurement unit 2. On the other hand, from time F onwards, it is in the future, so no glossiness measurement results have been obtained by the measurement unit 2. The multiple circles located up to time F indicate the glossiness measurement results at each time. By approximating these multiple measurement results, a solid approximation line extending to time F is obtained. Such an approximation line (a straight line in Figure 12) indicates the change trend.

13 is a diagram showing a specific example of a change trend of the value of the degree of contamination included in the environmental factor as an example of an estimation factor. The change trend changes before and after the time point H in FIG. 13. Such a change time point may be determined, for example, by calculating the rate of change of the degree of contamination at each time point (e.g., a differential value). If the value of the rate of change changes rapidly beyond a threshold value, or if the value of the rate of change changes rapidly so as to become discontinuous (e.g., if the difference between the rates of change at consecutive times exceeds a threshold value), that time point may be determined as the change time point. The diagnosis unit 5 determines the change trend after the time point H, which is the change time point, without using the measurement results of the degree of contamination before the time H. For example, the diagnosis unit 5 may determine the change trend of the degree of contamination after the time H based only on the estimated value between the time H and the time I. By determining the change trend in this way, the change trend can be determined more accurately.

According to at least one of the embodiments described above, the resistance value can be accurately predicted by having a measurement unit that measures at least one of the material factors related to the insulating material of the insulation resistor used in the target device or the amount of ionic pollutants attached among the environmental factors at the location where the insulation resistor is installed, and a resistance value estimation unit that estimates the resistance value of the insulation resistor using the measurement results.

The functions of the degradation diagnosis device, the measurement device, and the analysis device in each of the above-mentioned embodiments may be realized by a computer. In that case, a program for realizing this function may be recorded in a computer-readable recording medium, and the program recorded in the recording medium may be read into a computer system and executed to realize the functions. Note that the term "computer system" here includes hardware such as an OS and peripheral devices. Furthermore, the term "computer-readable recording medium" refers to portable media such as flexible disks, optical magnetic disks, ROMs, CD-ROMs, DVD-ROMs, and USB memories, and storage devices such as hard disks built into computer systems. Furthermore, the term "computer-readable recording medium" may include those that dynamically hold a program for a short period of time, such as a communication line when a program is transmitted via a network such as the Internet or a communication line such as a telephone line, and those that hold a program for a certain period of time, such as a volatile memory inside a computer system that is a server or client in such a case. The above-mentioned program may be for realizing part of the above-mentioned functions, and may further be capable of realizing the above-mentioned functions in combination with a program already recorded in the computer system.

When the measuring unit 2 in each embodiment is realized using a computer, the program causes the computer to execute the process of "a measurement process that controls the measurement of at least one of the following: a material factor related to the insulating material of the insulation resistor used in the target device, or an environmental factor at the location where the insulation resistor is installed, which is the amount of ionic pollutants attached." Then, under the control of the computer, the measurement of the physical quantity itself is performed by a sensor or the like described in the embodiment.

Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the gist of the invention. These embodiments and their modifications are within the scope of the invention and its equivalents as set forth in the claims, as well as the scope and gist of the invention.

1...deterioration diagnosis device, 2...measurement section, 3...resistance value estimation section, 4...validity determination section, 5...diagnosis section, 6...diagnosis result display section, 7...estimated formula memory section, 8...model-specific material name memory section, 11...analysis data memory section for creating estimated formula, 12...estimated formula creation section, 13...insulation resistance information memory section, 21...material factor measurement section, 22...environmental factor measurement section, 31...insulating plate, 32...first electrode, 33...second electrode, 34...contamination/water film, 40...quartz oscillator, 41, 42...deposits, 43, 44...electrodes, 50...ion component analysis device, 51...pure water supply unit, 52...pure water supply pipe, 53...pollutant collection tank, 54...sampling pipe, 56...three-way valve, 57...cleaning drain pipe, 58...component measurement unit, 100...deterioration diagnosis system, 110...measuring device, 111...measurement value transmission unit, 120...analysis device, 121...measurement value reception unit, 150...deterioration diagnosis system, 160...analysis device, 161...measurement value reception unit, 162...analysis data collection unit, 311...casing, 312, 313...target device, 314, 315...measurement unit, 316...intake port, 317...exhaust port, 318...measurement unit

Claims (13)

A measuring unit is provided for measuring at least one of a material factor related to an insulating material of an insulation resistor used in a target device, and an amount of adhesion of ionic pollutants among environmental factors at a location where the insulation resistor is installed,
A diagnostic system in which the measurement unit is installed within a housing in which the target device is installed, near an intake port provided in the housing through which air flows into the housing from outside the housing, and at a position closer to the intake port than halfway between the target device and the intake port. The diagnostic system according to claim 1, wherein the measurement unit measures the amount of the ionic contaminants adhered based on a change in the resonant frequency of the quartz crystal oscillator. The diagnostic system according to claim 1, wherein the measurement unit measures the degree of contamination of the ionic contaminant based on the measurement result, temperature, and humidity, based on a correlation between the measurement result, temperature, humidity, and the degree of contamination of the ionic contaminant. The diagnostic system of claim 1, wherein the measurement unit measures changes in the mass of non-ionic dust and predicts an increase in ionic dust based on the measurement results. an estimation equation storage unit that stores information about an estimation equation for estimating a resistance value of the insulation resistor based on a measurement result by the measurement unit;
a resistance value estimation unit that estimates the resistance value of the insulation resistor by using the measurement result by the measurement unit and the estimation equation stored in the estimation equation storage unit;
an insulation resistance information storage unit that stores one or more of a result of the resistance value estimation by the resistance value estimation unit, the material factors, and the environmental factors;
The diagnostic system of claim 1 further comprising: A transmission unit that transmits the measurement result of the measurement unit;
A receiving unit that receives the measurement result,
The diagnostic system according to claim 5 , wherein the resistance value estimating unit estimates the resistance value using a measurement result received by the receiving unit. The diagnostic system according to claim 6, further comprising an analysis data collection unit that writes the measurement results received by the receiving unit into a memory unit. The diagnostic system according to claim 5, further comprising a diagnostic unit that determines a trend of change over time of an estimated resistance value, which is a resistance value estimated by the resistance value estimation unit, based on the estimation results stored in the insulation resistance information storage unit. The diagnostic system according to claim 8, wherein the diagnostic unit estimates a future value of the resistance estimate based on the trend of the change. The diagnostic system according to claim 8, wherein the diagnostic unit determines the timing at which the estimated future resistance value falls below a predetermined lower threshold based on the trend of the change. The diagnostic system according to claim 8, wherein the diagnostic unit determines a time point at which the trend of change changes. a measuring step of measuring at least one of a material factor related to an insulating material of an insulation resistor used in a target device, and an amount of adhesion of ionic contaminants among environmental factors at a location where the insulation resistor is installed, by a measuring unit,
A resistance value estimation method in which the measuring unit is installed within a housing in which the target device is installed, near an intake port provided in the housing through which air flows into the housing from outside the housing, and at a position closer to the intake port than halfway between the target device and the intake port. A measuring unit is provided for measuring at least one of a material factor related to an insulating material of an insulation resistor used in a target device, and an amount of adhesion of ionic pollutants among environmental factors at a location where the insulation resistor is installed,
A computer program for causing a computer to function as a diagnostic system, wherein the measurement unit is installed within a housing in which the target device is installed, near an intake port provided in the housing through which air flows into the housing from outside the housing, and at a position closer to the intake port than halfway between the target device and the intake port.

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