JP6714844B2 - Abnormality diagnosis method - Google Patents

Description

The present invention relates to an abnormality diagnosis method for diagnosing an abnormality in mechanical equipment.

2. Description of the Related Art Conventionally, rotating parts such as railway vehicles and wind turbines for power generation are inspected for defects such as damage and wear on bearing devices and other parts after a certain period of use. This inspection is performed by periodically disassembling the entire apparatus, and damage or wear on the rotating parts is visually detected by an inspector.

However, it takes a considerable amount of time and cost to inspect such rotating parts for abnormalities. Therefore, there has been proposed a method capable of diagnosing an abnormality of a rotating component in an actual operating state without disassembling a mechanical device in which the rotating component is incorporated (for example, refer to Patent Document 1). In this diagnostic method, damage is detected during operation. Therefore, the time interval of shock vibration caused by damage is calculated from the vibration signal (acceleration, sound, etc.) and compared with the theoretical value calculated from the bearing specifications for diagnosis. Yes, the absolute criterion is used. There is also a method in which the same part is periodically compared in time series, and a value in a normal case is used as an initial value, and a relative judgment criterion is used to judge how many times the value has increased.

Japanese Patent No. 4117500

By the way, in the wind turbine generator, the wind direction and the wind force are constantly changing, so the rotation speed and load of the shaft supported by the bearing are constantly changing, and the generator load is also changing. In such a situation, the output from the sensor will include the effects of varying environmental conditions.

Therefore, in order to diagnose abnormalities such as rolling bearings by the sensor using the absolute judgment standard, it is necessary to set the index for each condition, and it is very difficult to set the index for all conditions. I need time. Further, when using the relative judgment criterion, it is necessary to save the time-series data, so that the storage capacity may become enormous, and the environmental condition may cause a false diagnosis.

The present invention has been made in view of the above problems, and an object thereof is to provide an abnormality diagnosis method capable of efficiently diagnosing an abnormality even when environmental conditions change.

The above object of the present invention is achieved by the following configurations.
(1) An acquisition step of acquiring a vibration effective value of each of the rolling bearings, which is a common part of each of the mechanical equipments, during operation of three or more mechanical equipments including a spindle and a rolling bearing that supports the spindles.
A comparison step of mutually comparing the vibration effective value of the rolling bearing of each of the mechanical equipment, between the respective mechanical equipment ,
In the comparison step, one of the effective vibration values of the rolling bearing in each of the mechanical equipment is a dmn value obtained by multiplying the pitch circle diameter of the rolling bearing by the rotation speed of the main shaft supported by the rolling bearing. It is performed when the threshold value that is determined depending on is exceeded,
An abnormality diagnosis method characterized by the above.
(2) The abnormality diagnosis according to (1), wherein the mechanical equipment is any one of a wind turbine, a railroad vehicle, a pump, and an air conditioning equipment in which an environmental condition that changes the effective vibration value changes during operation. Method.

According to the abnormality diagnosis method of the present invention, it is possible to easily diagnose the abnormality between the common parts of the mechanical equipment by acquiring the physical quantities of the common parts of the mechanical equipment and comparing them. As a result, compared with the diagnosis by the absolute judgment standard that sets the absolute standard of each judgment index according to the condition or the diagnosis by the relative judgment standard that needs to save the time series data, the environmental conditions can be reduced while suppressing the data storage capacity. Even if it fluctuates, the abnormality can be efficiently diagnosed.

1 is a schematic configuration diagram of a plurality of wind turbine generators to which an abnormality diagnosis method according to the present invention is applied. It is a block diagram explaining a rolling bearing of a wind power generator and a control system which performs abnormality diagnosis. It is a flowchart explaining the abnormality diagnosis method which concerns on this invention. It is a graph which shows an example of a time series change of the vibration effective value of a rolling bearing. It is a graph which shows an example of the relationship between the frequency and amplitude of vibration of a rolling bearing. It is a graph which shows the other example of the relationship between the frequency and amplitude of vibration of a rolling bearing. It is a flow chart explaining the abnormality diagnostic method concerning the modification of the present invention. It is a graph which shows an example of the relationship between the frequency and amplitude of vibration of the rolling bearing of a modification.

Hereinafter, an embodiment of an abnormality diagnosis method according to the present invention will be described with reference to the drawings.

The abnormality diagnosis method according to the present embodiment is, for example, a method of monitoring the presence/absence of an abnormality during operation of mechanical equipment such as a wind turbine generator. When three or more machines with the same specifications are operating under the same conditions, the vibration signals (acceleration, sound, etc.) of the same parts of the machines are measured, and the judgment index (effective value of envelope spectrum or Peak value) is analyzed. The obtained values are compared with each other between the mechanical equipment, and it is specified that damage (peeling, scratches, electrolytic corrosion, etc.) has occurred at the measurement site of the mechanical equipment that shows a value that deviates from others. As a result, it is possible to efficiently diagnose even when the influence of changing environmental conditions is included, and to reduce the data storage capacity of vibration information, without setting the absolute reference of each determination index for each condition.

In the following description, as the mechanical equipment, for example, a wind turbine generator including a rolling bearing will be described as an example.
FIG. 1 is a schematic configuration diagram of a plurality of wind turbine generators to which the abnormality diagnosis method according to the present invention is applied.

As shown in FIG. 1, the abnormality diagnosis method according to the present embodiment diagnoses the presence or absence of abnormality in three wind turbine generators (wind turbines) 10A, 10B, 10C.

The wind turbine generators 10A, 10B, and 10C each include a tower 11 that is erected on the ground, a nacelle 12 that is supported at the upper end of the tower 11, and a blade 13 that is provided at the end of the nacelle 12. It has the same specifications.

The drive train unit 21 is stored in the nacelle 12. The drive train unit 21 includes a main shaft 22, a speed increaser 23, a generator 24, and a rolling bearing 25. The main shaft 22 is connected to a generator 24 via a gearbox 23. The main shaft 22 is rotatably supported in the nacelle 12 by a rolling bearing 25. A vibration sensor 27 is provided on the rolling bearing 25 that supports the main shaft 22.

The blade 13 has a hub 31 and a plurality of blades 32. The blades 32 extend radially from the hub 31. The blade 13 is provided at the end of the main shaft 22 of the drive train unit 21.

In the wind turbine generators 10A, 10B, and 10C, the rotation shafts of the speed increaser 23 and the generator 24 are also supported by rolling bearings (not shown). Further, the drive train unit 21 is provided with a brake device (not shown) that stops the rotation of the main shaft 22 as necessary.

In the wind turbine generators 10A, 10B, and 10C having the above structure, the main shaft 22 is rotated by the blade 32 of the blade 13 receiving wind. Then, the rotation of the main shaft 22 is increased in speed by the speed increaser 23, transmitted to the generator 24, and generated by the generator 24. These wind turbine generators 10A, 10B and 10C are operated under the same conditions.

FIG. 2 is a block diagram illustrating a rolling bearing of a wind turbine generator and a control system that performs abnormality diagnosis.
As shown in FIG. 2, the rolling bearing 25 is arranged so that it can be rolled between the inner ring 41 that is externally fitted to the main shaft 22 to rotate, the outer ring 42 that is internally fitted to a housing or the like, and the inner ring 41 and the outer ring 42. A plurality of rolling elements 43 and a retainer (not shown) that holds the rolling elements 43 in a rollable manner. In this embodiment, a spherical roller bearing is used as the rolling bearing 25 that supports the main shaft 22.

The vibration sensor 27 is fixed to the housing load zone of the outer ring 42, which is the fixed ring of the rolling bearing 25. As the vibration sensor 27, in addition to an acceleration sensor, for example, an AE (Acoustic Emission) sensor, an ultrasonic sensor, a shock pulse sensor, or the like can be used, and it is also possible to detect acceleration, velocity, strain, stress, displacement, or the like. Therefore, a device capable of equivalently detecting sound or vibration and converting it into an electric signal can be appropriately used.

Note that the vibration sensor 27 can be fixed by bolt fixing, bonding, a combination of bolt fixing and bonding, embedding with a resin material, installation using a magnet, or the like.

The vibration sensor 27 is connected to the controller 50 via the data transmission means 51, detects the vibration of the rolling bearing 25, and transmits the vibration information to the controller 50 as an electric signal. The controller 50 has an arithmetic processing unit 52 and a control device 53. The arithmetic processing unit 52 calculates the effective value and amplitude of the vibration of the rolling bearing 25 based on the vibration information transmitted from the vibration sensor 27 to perform abnormality diagnosis. The controller 53 drives and controls the wind turbine generators 10A, 10B, 10C. Further, the controller 50 is connected to an output device 54 including a monitor and an alarm.

The vibration sensor 27 has a built-in amplifier that amplifies and outputs the output signal. The amplifying means for amplifying the sensor output may be connected between the vibration sensor 27 and the microcomputer constituting the controller 50, or may be built in the microcomputer side. However, in the case where the amplifier is built in the vibration sensor 27, since the output signal of the vibration sensor 27 is strong, it is possible to suppress the influence of noise added in the signal transmission path between the vibration sensor 27 and the microcomputer. Therefore, it is possible to suppress deterioration of processing accuracy due to noise and improve reliability of abnormality diagnosis.

Next, an abnormality diagnosis method of the present invention for performing abnormality diagnosis of the three wind turbine generators 10A, 10B, 10C having the same specifications will be described.
FIG. 3 is a flow chart for explaining the abnormality diagnosis method according to the present invention.

First, in a state where the wind turbine generators 10A, 10B, and 10C are operated under the same conditions, vibration information, which is a physical quantity of the rolling bearing 25 that is a common part of the wind turbine generators 10A, 10B, and 10C, is acquired from the vibration sensor 27. (Step S1).

Furthermore, the acquired vibration information of the rolling bearing 25 of each wind power generator 10A, 10B, 10C is monitored. Specifically, it is monitored whether or not the effective vibration value of the rolling bearing 25 of any of the wind turbine generators 10A, 10B, 10C exceeds a preset threshold value (step S2). Note that the threshold value is generally determined depending on the dmn value obtained by multiplying the pitch circle diameter of the rolling bearing 25 and the rotation speed of the main shaft 22.

When it is determined that the effective vibration value of the rolling bearing 25 of any of the wind turbine generators 10A, 10B, 10C exceeds the threshold value (step S2: Yes), the rolling bearings of the wind turbine generators 10A, 10B, 10C. The frequency analysis of 25 is performed (step S3), and the vibration information of the rolling bearing 25 is mutually compared among the wind turbine generators 10A, 10B, and 10C (step S4).

FIG. 4 is a graph showing an example of a time series change of the effective vibration value of the rolling bearing.
In FIG. 4, the effective vibration value of the wind turbine generator 10C gradually increases and exceeds the threshold value. Thus, for example, when the effective vibration value of the wind turbine generator 10C exceeds the threshold value, the vibration information is mutually compared among the wind turbine generators 10A, 10B, 10C.

The mutual comparison among the wind turbine generators 10A, 10B, 10C is performed by collecting and analyzing the vibration information acquired at the end of all the wind turbine generators 10A, 10B, 10C. Specifically, after performing bandpass filter processing (absolute value detection to envelope processing), envelope processing is performed, and an FFT power spectrum is calculated from the waveform, and an amplitude peak (frequency peak) that is a determination index is calculated. The wind power generators 10A, 10B, and 10C are compared with each other. Since this peak changes in frequency and level depending on the rotation speed of the main shaft 22 in which the rolling bearing 25 is installed, the rotation information of the rolling bearing 25 (the rotation speed of the main shaft 22) is acquired at the same time as the vibration information, and the rolling bearing is obtained. It is calculated from 25 specifications.
Also, wavelet transform or Hilbert transform may be performed instead of the envelope process.

As a result of the mutual comparison of the amplitude peaks, it is determined whether or not the vibration peaks of the rolling bearings 25 of all the wind turbine generators 10A, 10B, 10C show the same tendency (step S5).

In the determination of the peak of the amplitude, when it is determined that the rolling bearings 25 of the wind turbine generators 10A, 10B, 10C all show the same tendency (step S5: Yes), the wind turbine generators 10A, 10B, 10C It is determined that there is no abnormality in the rolling bearing 25 (step S6).

On the other hand, in the determination of the peak of the amplitude, when it is determined that any one of the wind turbine generators 10A, 10B, and 10C shows a tendency to deviate from the other (step S5: No), it will be described later in step S7. After identifying the abnormal part, it is determined that one rolling bearing 25, which tends to deviate from the other of the wind turbine generators 10A, 10B, and 10C, is abnormal (step S8).

FIG. 5 is a graph showing an example of the relationship between the frequency and the amplitude of vibration of the rolling bearing. FIG. 6 is a graph showing another example of the relationship between the vibration frequency and the amplitude of the rolling bearing.

In FIG. 5, a wind power generation device 10C has a peak of amplitude that does not appear in the other wind power generation devices 10A and 10B, and at this peak, there is a deviation from the other wind power generation devices 10A and 10B. Therefore, in this case, it is determined that the rolling bearing 25 of the wind turbine generator 10C has an abnormality such as damage.

Further, in FIG. 6, peaks are generated in each of the wind turbine generators 10A, 10B, 10C, but the peak of the wind turbine generator 10C is different in frequency from the peaks of the other wind turbine generators 10A, 10B. .. Therefore, also in this case, it is determined that the rolling bearing 25 of the wind turbine generator 10C has an abnormality such as damage due to wear (eg, peeling, scratch, electrolytic corrosion, etc.).

When it is determined that there is an abnormality in any of the wind turbine generators 10A, 10B, and 10C, an alarm signal is transmitted to the output device 54, and the output device 54 issues a sound alarm or notifies the output device 54 of the abnormality. A message is displayed to warn the administrator (step S9).

In addition, in this embodiment, when it determines with any one of the wind power generators 10A, 10B, and 10C showing the tendency which deviated from others (step S5: No), in step S7, a peak generation frequency is shown. Of the machine element is searched for the abnormal theoretical frequency of the machine element, and which machine element in the device causes the abnormality. In the case of a wind turbine generator, each abnormal component may be a component of a rolling bearing, a gear, or a blade.

As described above, according to the abnormality diagnosis method of the present embodiment, by detecting the vibration information of the rolling bearing 25, which is a common part of the wind turbine generators 10A, 10B, and 10C, and comparing the vibration information with each other, the wind Abnormalities between the rolling bearings 25 of the power generators 10A, 10B, 10C can be easily diagnosed. As a result, compared with the diagnosis by the absolute judgment standard that sets the absolute standard of each judgment index according to the condition or the diagnosis by the relative judgment standard that needs to save the time series data, the environmental conditions can be reduced while suppressing the data storage capacity. Even if it fluctuates, the abnormality can be efficiently diagnosed. In particular, the present embodiment is suitable for abnormality diagnosis of a wind turbine generator that is a wind turbine that is easily affected by environmental conditions such as wind speed and wind direction.

Further, in the present embodiment, when any one of the effective vibration values of the rolling bearings 25 of the wind turbine generators 10A, 10B, 10C exceeds the threshold value, the vibration information of the rolling bearings 25 of the wind turbine generators 10A, 10B, 10C. By comparing with each other, the abnormality in the rolling bearing 25 of the wind turbine generators 10A, 10B, 10C can be efficiently diagnosed.

Further, by issuing a warning when diagnosing that the rolling bearing 25 of each wind power generator 10A, 10B, 10C has an abnormality, the abnormality of the rolling bearing 25 of the wind power generators 10A, 10B, 10C is quickly managed by the warning. Can inform the person.

In addition, in the said embodiment, although the mechanical equipment which consists of wind power generators 10A, 10B, and 10C was made into the object of abnormality determination, the abnormality diagnosis method of this invention is not limited to a wind power generator, For example, a rail vehicle. , Pumps, air-conditioning equipment, and all other mechanical equipment of the same specifications including rolling bearings that are operated under three or more under the same conditions.

Further, the common part for abnormality diagnosis is not limited to a rolling bearing including an outer ring, an inner ring and a rolling element, but may be another type of rolling bearing, and is not limited to a rolling bearing, and a gear applicable to a rotary machine. Alternatively, it may be a rotating component such as a blade of a wind turbine.

Further, in the present embodiment, the case where the mechanical equipment including the three wind turbine generators 10A, 10B, and 10C is installed is illustrated, but the number of mechanical equipment is not limited to three as long as it is three or more.

In this embodiment, vibration information is used as a physical quantity for abnormality diagnosis, but vibration, sound, ultrasonic waves, stress, displacement or strain can be used as the physical quantity used for abnormality diagnosis.

FIG. 7 is a flowchart according to a modified example of this embodiment. This modification is different from the above embodiment in that the deviation between the peak of the envelope spectrum of the separated apparatus and the base level is confirmed in step S10. Further, also in this modification, the site for abnormality diagnosis is not limited to the rolling bearing.

That is, when it is determined that any one of the wind turbine generators 10A, 10B, and 10C exhibits a tendency to deviate from the others (step S5: No), the peak of the envelope spectrum and the base level of the deviated apparatus are determined. The deviation is confirmed in step S10 (see FIG. 8). Then, as a result of the confirmation, if the deviation value is less than the threshold value at one or more frequencies, it is determined that no abnormality has occurred (step S11). On the other hand, when the deviation value is equal to or more than the threshold value at one or more frequencies, it is determined that an abnormality has occurred in the mechanical element (step S8). Thereby, the diagnostic accuracy can be further improved. Further, the deviation value may be determined to be abnormal when the deviation value is equal to or more than the threshold value at one or more frequencies. However, when the deviation value is equal to or more than the threshold values at a plurality of frequencies, it is determined to be abnormal, thereby further diagnosis is performed. The accuracy can be improved.

10A, 10B, 10C Wind power generator (mechanical equipment)
25 Rolling bearing 41 Inner ring 42 Outer ring 43 Rolling element

Claims (2)

An acquisition step of respectively acquiring a vibration effective value of the rolling bearing, which is a common part of each of the mechanical equipment, when operating three or more mechanical equipment including a main shaft and a rolling bearing that supports the main shaft;
A comparison step of mutually comparing the vibration effective value of the rolling bearing of each of the mechanical equipment, between the respective mechanical equipment ,
In the comparison step, one of the effective vibration values of the rolling bearing in each of the mechanical equipment is a dmn value obtained by multiplying the pitch circle diameter of the rolling bearing by the rotation speed of the main shaft supported by the rolling bearing. It is performed when a threshold that is determined depending on is exceeded,
An abnormality diagnosis method characterized by the above. The abnormality diagnosis method according to claim 1, wherein the mechanical equipment is any one of a wind turbine, a rail vehicle, a pump, and an air conditioning equipment in which an environmental condition that changes the vibration effective value changes during operation.

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