TWI802234B - Displacement sensor and state monitoring method - Google Patents

Abstract

The present invention provides a displacement sensor and a state monitoring method capable of appropriately determining an abnormality type. A displacement sensor includes: a light projection part projecting light to an inspection area; a light receiving part receiving the light reflected by the inspection area and outputs a light receiving waveform; a memory part pre-storing abnormality type information and establishes correspondences between a plurality of feature information included in the light receiving waveform and a plurality of abnormality types predicted according to the plurality of feature information; a feature information extraction part extracting at least two or more types of feature information based on the light receiving waveform output by the light receiving part; an abnormality type determination part determining at least one or more abnormality type among a plurality of abnormality types in the abnormality type information pre-stored in the memory part based on the extracted at least two or more feature information; and an output part outputting the determined abnormality type.

Description

Displacement sensor and state monitoring method

The invention relates to a displacement sensor and a state monitoring method.

Conventionally, as a device for measuring the displacement of a workpiece in a non-contact manner, a displacement sensor using an optical system has been used. In such a displacement sensor, there is a possibility that a workpiece cannot be properly measured due to aging deterioration of the displacement sensor, contamination due to adhesion of foreign matter such as dust, and influence of the surrounding environment.

Patent Document 1 discloses a technique related to a detection device that detects a symptom of an abnormality of a detection sensor before an abnormality of the detection sensor occurs. Specifically, the detection device disclosed in Patent Document 1 acquires the amount of light received by the light receiving unit when the workpiece passes the detection position every predetermined period, and generates a light reception waveform based on the acquired result. Moreover, the detection device judges whether there is any abnormality symptom in the detection sensor by comparing the reference waveform with the light receiving waveform. [Prior art literature] [Patent Document]

Patent Document 1: Japanese Patent Laid-Open No. 2012-78175

[Problem to be Solved by the Invention]

However, in the detection device disclosed in Patent Document 1, although it is determined whether there is a sign of abnormality in the detection sensor, even if a sign of abnormality is detected, the cause and details cannot be distinguished. Therefore, there is a problem in that it is impossible for the user to grasp what specific countermeasures need to be taken thereafter.

Therefore, an object of the present invention is to provide a displacement sensor and a state monitoring method that can appropriately determine the type of abnormality. [Means to solve the problem]

A displacement sensor according to an aspect of the present invention includes: a light projecting unit that projects light to the inspection area; a light receiving unit that receives light reflected from the inspection area and outputs a received light waveform; a memory unit that stores abnormality category information in advance, and the abnormality category The information establishes a correspondence between a plurality of feature information contained in the light receiving waveform and a plurality of abnormal categories predicted according to the plurality of feature information; the feature information extraction part extracts at least two or more types of abnormalities based on the light receiving waveform output by the light receiving part feature information; the abnormal category determination unit determines at least one abnormal category among the plurality of abnormal categories in the abnormal category information pre-stored in the memory unit based on at least two or more extracted feature information; and an output unit, The determined abnormality category is output. Here, the light-receiving waveform refers to light received by the imaging element of the light-receiving unit, which displays the light-receiving amount for each pixel and is generated as a waveform.

According to the above aspect, the characteristic information extraction unit extracts at least two or more characteristic information based on the received light waveform, and the abnormality type determination unit can appropriately determine a plurality of abnormality types among the abnormality type information previously stored in the memory unit based on the characteristic information. At least one of the above exception classes in .

In the above aspect, the abnormality type determination unit may use at least one of the difference from the reference value, the amount of change within a predetermined period, and the information contained in the light-receiving waveform at a certain time point for the extracted feature information. The above monitoring method is used to determine the abnormal category.

According to the above aspect, since the abnormality type determination unit determines the abnormality type using a plurality of monitoring methods, the abnormality type can be determined more appropriately.

In the above-mentioned aspect, among the plurality of abnormality types in the abnormality type information stored in the storage unit in advance, the abnormality types that can be determined according to the monitoring method may be classified.

According to the above aspect, since the abnormality types that can be determined according to the monitoring method are classified in the abnormality type information, an appropriate monitoring method can be used for each abnormality type in determining the abnormality type.

In the above form, the characteristic information may also include at least two or more of the received light amount, the light received amount adjustment parameter, the width value of the light received waveform, the number of planes of the light received waveform, the total area of the light received waveform, and the background level.

According to the form, the feature information extracted from the light receiving waveform includes at least two of the received light amount, the light receiving amount adjustment parameter, the width value of the light receiving waveform, the number of planes of the light receiving waveform, the total area of the light receiving waveform, and the background level. There are more than one type, so based on these, the abnormality category can be determined more specifically.

In the above-mentioned form, the abnormal type determination unit may also be based on a combination of at least two or more of the light receiving amount, the light receiving amount adjustment parameter, the width value of the light receiving waveform, the number of planes of the light receiving waveform, the total area of the light receiving waveform, and the background level To determine the abnormal category.

According to the above aspect, since the abnormality type determination unit appropriately combines a plurality of feature information to determine each abnormality type, it is possible to more appropriately determine the abnormality type.

In the above-mentioned form, it is also possible to adjust the parameter, the width value of the light-receiving waveform, the number of planes of the light-receiving waveform, and the number of light-receiving waveforms according to the amount of light received, the light-receiving amount, and the number of planes of the light-receiving waveform among the plurality of abnormality types in the abnormality type information pre-stored in the memory unit. The total area of , and at least two types of abnormalities determined by combination of background levels are classified.

According to the above-mentioned form, in the abnormal type information, at least two or more of the abnormality types can be selected according to the amount of light received, the adjustment parameter of the light received amount, the width value of the light received waveform, the number of planes of the light received waveform, the total area of the light received waveform, and the background level. By combining and classifying the determined abnormality types, it is possible to appropriately combine feature information according to each abnormality type in determining the abnormality type.

In the above-described aspect, among the plurality of abnormality types stored in the storage unit in advance, the abnormality types that can be determined based on the presence or absence of workpieces and bases in the inspection area may be classified. In addition, the term "work" refers to an object to be measured by the displacement sensor, and the term "pedestal" refers to a pedestal on which the workpiece is placed.

According to the above aspect, it is possible to appropriately determine the type of abnormality according to the presence or absence of the workpiece and the base in the inspection area.

In the above-mentioned aspect, the output unit may output a countermeasure or an estimated cause corresponding to the determined abnormality type.

According to the above configuration, the user can directly and concretely grasp what kind of countermeasures should be taken.

A state monitoring method according to the present invention is a state monitoring method performed by a displacement sensor including a processor, including: a light projecting step, projecting light to the inspection area; a light receiving step, receiving light reflected by the inspection area, and outputting the received light Waveform; the feature information extraction step, based on the light receiving waveform output in the light receiving step, extracting at least two or more feature information; the abnormal category determination step, based on the extracted at least two or more feature information, judging that it is stored in memory in advance At least one abnormal category among the multiple abnormal categories in the abnormal category information, the abnormal category information establishes a plurality of characteristic information contained in the light receiving waveform and a plurality of abnormal categories predicted according to the plurality of characteristic information Corresponding; and an output step of outputting the determined abnormal category.

According to the above aspect, in the feature information extraction step, at least two or more feature information are extracted based on the received light waveform, and in the abnormality type determination step, based on the feature information, it is possible to appropriately determine the abnormality type information stored in the memory in advance. At least one or more exception classes among the plurality of exception classes. [Effect of the invention]

According to the present invention, it is possible to provide a displacement sensor and a state monitoring method capable of appropriately determining the type of abnormality.

Hereinafter, embodiments of the present invention will be specifically described with reference to the drawings. In addition, the embodiment described below is only a specific example for carrying out the present invention, and the present invention is not limitedly interpreted. In addition, in order to facilitate understanding and description, the same components may be given the same reference numerals as much as possible in each drawing, and overlapping descriptions may be omitted.

<An embodiment> [Structure of Displacement Sensor] FIG. 1 is a perspective view showing the appearance of a sensor head of a displacement sensor 10 according to an embodiment of the present invention. The displacement sensor 10 includes a sensor head 100 and an auxiliary housing (not shown) called an amplifier unit connected via a cable 11 .

As shown in FIG. 1 , the displacement sensor 10 projects laser light L1 from the sensor head 100 to the workpiece W placed on the base B, and receives reflected light L2 from the workpiece W relative to the laser light L1, thereby based on The principle of triangulation distance measurement is used to measure the displacement of the surface of the workpiece W. In addition, the distance from the sensor head 100 to the surface of the workpiece W is measured as the displacement amount, and the distance can be output as detection data.

In this way, the displacement sensor 10 is usually a measuring device that projects laser light on the workpiece W to measure the displacement of the surface of the workpiece W. However, in this embodiment, for example, when the workpiece W does not exist, the assembled Equipment such as a robot or the surrounding environment is used as an inspection area, and laser light is projected and reflected light from the inspection area is received for status monitoring.

FIG. 2 is a block diagram showing functions constituting the displacement sensor 10 according to one embodiment of the present invention. As shown in FIG. 2 , the displacement sensor 10 includes: a light projecting unit 110 , a light receiving unit 120 , a control unit 130 , a memory unit 140 , a display unit 150 , an operation unit 160 and an input/output interface 170 . In addition, the light projecting unit 110 includes a light emitting element 111 and a light projecting control circuit 112 , and the light receiving unit 120 includes an imaging device 121 , a signal processing circuit 122 and an analog/digital (A/D) conversion circuit 123 .

In addition, for example, the light projecting part 110, the light receiving part 120, the control part 130 and the memory part 140 are incorporated into the sensor head 100 shown in FIG. . However, the separation of the sensor head 100 and the amplifier unit is not necessarily required, and all the structures shown in FIG. 2 may be provided in one housing.

The light projecting unit 110 projects light onto an inspection area (for example, workpiece W). Specifically, the light projecting unit 110 has a laser diode (LD) as a light emitting element 111 , and the light emitting control circuit 112 adjusts the luminous intensity and light emitting time of the light emitting element 111 and simultaneously drives the light emitting element 111 to cast light. In addition, the light projection control circuit 112 operates based on an instruction from the control unit 130 .

The light receiving unit 120 receives light reflected by the inspection region, and outputs a light-receiving waveform. More specifically, the light receiving unit 120 has a complementary metal oxide semiconductor (Complementary Metal Oxide Semiconductor, CMOS) including a plurality of pixels as the imaging element 121, and further includes an image sensor for processing the image signal generated by the imaging element 121. The signal processing circuit 122 and the A/D conversion circuit 123. The signal processing circuit 122 controls the timing of the operation of the imaging element 121 based on an instruction from the control unit 130 , and takes in an image generated by the imaging element 121 and outputs it to the A/D conversion circuit 123 . Furthermore, the image converted from analog to digital by the A/D conversion circuit 123 is input to the control unit 130 for measurement processing.

The control unit 130 is, for example, a central processing unit (CPU), and based on the programs and setting data stored in the memory unit 140, emits the laser light L1 from the light projecting unit 110, and makes the light receiving unit L1 output according to the timing of its emission. 120 operates to receive reflected light L2 from the workpiece W. Furthermore, the control unit 130 measures the amount of displacement of the workpiece W by various processes based on the light reception waveform output from the light receiving unit 120 .

In addition, the control unit 130 extracts feature information contained in the light-receiving waveform based on the light-receiving waveform output from the light-receiving unit 120 , and determines the type of abnormality estimated from the extracted feature information. The details of the process of determining the type of abnormality in the displacement sensor 10 will be described later.

The storage unit 140 is, for example, a non-volatile memory such as an Electronically Erasable Programmable Read Only Memory (EEPROM), and stores programs and settings for defining various actions controlled by the control unit 130. data, and abnormality type information used in the process of determining the abnormality type described later. In addition, it is also set as a buffer function for accumulating data such as a light-receiving waveform output from the light-receiving unit 120 and characteristic information extracted from the light-receiving waveform, which are used in the process of determining the type of abnormality described later.

The display unit 150 includes, for example, a liquid crystal display or an organic electroluminescence (electroluminescent, EL) display, etc., and displays the measurement value in the control unit 130 described above, the abnormality category described later, the countermeasure corresponding to the abnormality category, and other information. The relevant setting and status of the displacement sensor 10 should be notified to the user.

The operation unit 160 includes buttons, a dial, or a touch panel, and is used, for example, to enable the user to turn on and off the power of the displacement sensor 10 , to switch various settings, and to switch operation modes.

The input-output interface 170 is connected to external machines such as a personal computer (PC) and a programmable logic controller (programmable logic controller, PLC). In the case of connecting to an external device, the same operations as those performed in the operation unit 160 can be performed by the external device. For example, the operation of the displacement sensor 10 can also be performed on a PC, or the measurement results obtained by the displacement sensor 10 can be displayed on the screen of the PC.

[Determination processing of abnormal type] Next, the determination process of the abnormality type performed by the displacement sensor 10 will be described in detail. Here, the determination process of the abnormal type is, for example, detecting that the workpiece W cannot be properly measured due to the deterioration of the displacement sensor 10 over time, contamination caused by adhesion of foreign matter such as dust, and the influence of the surrounding environment. A function that is abnormal or a symptom thereof.

FIG. 3 is a block diagram showing the configuration of the control unit 130 related to the determination process of the abnormality type and its related functions in the displacement sensor 10 according to the embodiment of the present invention. As shown in FIG. 3 , the control unit 130 includes a characteristic information extraction unit 131 , an abnormality category determination unit 132 , and an output unit 133 , and refers to the storage unit 140 to determine the abnormality category and output it to the display unit 150 .

The feature information extraction unit 131 extracts at least two or more types of feature information based on the light receiving waveform output by the light receiving unit 120 . Specifically, the feature information extraction unit 131 extracts a plurality of pieces of feature information to be described later based on the light receiving waveform continuously output from the light receiving unit 120 , and stores them in the memory unit 140 .

FIG. 4 is a diagram showing an example of feature information extracted from a light-receiving waveform. In the figure, the pixel position represents the position of the pixel on the imaging device 121 in the base line direction in triangulation distance measurement, and LSB represents the average value of the received light amount of the pixel corresponding to the pixel position. As shown in Figure 4, (1) light receiving amount, (2) light receiving amount adjustment parameter, (3) width value, (4) surface number, (5) total area of light receiving waveform, and (6) background position are extracted from the light receiving waveform allow.

In addition, (1) received light amount refers to the received light amount of light received by the light receiving unit 120 , and (2) received light amount adjustment parameter refers to the output required as a light received waveform according to the received light amount of light received by the light receiving unit 120 . adjustments (for example, gain and exposure time, etc.). The so-called (3) width value is, for example, the width (half-value width) of the light-receiving waveform at 50% of the peak light-receiving amount, and represents the expansion of the light-receiving waveform, and the so-called (4) surface number is the number of peaks of the light-receiving waveform, and represents the number of light components. (5) The total area of the light-receiving waveform is, for example, the total area of the range occupied by the light-receiving waveform shown in FIG. 120 The amount of received light obtained from the received ambient light. In addition, the width value is not limited to the width (half-value width) of the received light waveform at 50% of the peak received light amount, and may be at, for example, 40% or 60% of the peak received light amount as long as it represents the expansion of the received light waveform. Width value of received light waveform.

In this way, in this embodiment, the characteristic information extraction unit 131 extracts the six characteristic information (1) to (6) above from the light receiving waveform output by the light receiving unit 120 .

The abnormal type determination unit 132 determines at least one abnormal type among the plurality of abnormal types in the abnormal type information stored in the storage unit 140 based on at least two or more types of feature information extracted by the feature information extraction unit 131 . Specifically, the abnormality type determination unit 132 determines the abnormality type based on the feature information of (1) to (6) described above, based on their time-series changes, their combinations, and the like. Here, abnormality type information that associates a plurality of characteristic information included in the light-receiving waveform with a plurality of abnormality types predicted based on the plurality of characteristic information is stored in advance in the storage unit 140 . The abnormality type determination unit 132 determines the abnormality type by referring to the abnormality type information stored in the storage unit 140 based on the characteristic information of (1) to (6) described above.

FIG. 5 is a diagram showing an example of abnormality type information in which characteristic information is associated with an abnormality type predicted from the characteristic information. As shown in Figure 5, (a) sensor contamination, (b) light source degradation, (c) narrow part measurement, (d) multiple reflections, (e) mutual interference, and (f) interference light are six abnormal categories It is the value based on (1) light receiving amount, (2) light receiving amount adjustment parameter, (3) width value, (4) surface number, (5) total area of light receiving waveform, and (6) background level as mentioned above , time series changes, and combinations of these.

Furthermore, when the abnormality types (a) to (f) described above are determined based on the characteristic information of (1) to (6) above by using the abnormality type determination unit 132 , which monitoring method to use Judgment is made and correspondence is established. In the monitoring method, for example, the feature information of (1) to (6) above includes (A) the difference between the reference value, (B) the amount of change within a predetermined period, and (C ) A method of monitoring information contained in the received light waveform at a certain time point, at least one of these monitoring methods can be used.

The difference between (A) and the reference value is to compare the initial value of the displacement sensor 10 (for example, the value at the time of shipment from the factory, at the time of purchase, or at the time of starting operation) as a reference value (threshold value). (B) The amount of change within a specified period is to monitor the amount of change in a short period of several ms to hundreds of ms. The so-called (C) information contained in the light-receiving waveform at a certain point in time is to detect the temporary The information contained in the waveform of the .

Hereinafter, the abnormality types (a) to (f) will be described in detail by showing specific examples.

FIG. 6A is a diagram showing an example of determination (a) sensor contamination. As shown in FIG. 6A , in the presence of sensor contamination, the amount of light received, the parameter for adjusting the amount of light received, and the width value change (short-time sudden change). Furthermore, when the displacement sensor 10 exceeds the range of a reference value (threshold value) that can appropriately measure a workpiece, the abnormality type determination unit 132 determines that the sensor is contaminated. In addition, if the light-receiving waveform contains noise components, the total area of the light-receiving waveform and the background level may change, and these characteristic information can also be used to determine sensor contamination.

FIG. 6B is a diagram showing an example of determination (b) of light source degradation. As shown in FIG. 6B , when there is degradation of the light source, as the displacement sensor 10 , the amount of light received decreases and the gain as an adjustment parameter of the amount of light is increased. For example, when compared with the initial reference value of the displacement sensor 10 (a value detected by teaching at the time of first use) and exceeds the range of the reference value (threshold value) that can properly measure the workpiece Next, the abnormality type determination unit 132 determines that the light source is degraded.

FIG. 6C is a diagram showing an example of determination (c) stenosis measurement. As shown in FIG. 6C , in the case of measurement of a narrow part, as the displacement sensor 10, the state of the amount of light received may become unstable, and the waveform of the received light may fluctuate up and down, or the amount of light received may decrease and the adjustment parameter of the amount of light received may be adjusted. The state of gain increase. Here, the difference from (b) light source deterioration mentioned above is that in the case of measurement of a narrow part, for example, if the state of the inspection area (workpiece) changes (if it deviates from the position of the narrow part), the light received The adjustment parameters of light intensity and received light intensity are restored. In other words, when there is a stenosis measurement, if the received light amount and the change in the light received amount adjustment parameter are temporary, the abnormality type determination unit 132 determines that it is a stenosis measurement.

FIG. 6D is a diagram showing an example of determination (d) of multiple reflections. As shown in FIG. 6D , in the presence of multiple reflections, a change (short-term sudden change) like an increase in the number of faces occurs. Furthermore, when the displacement sensor 10 exceeds the range of the reference value (threshold value) of the measured value that can be properly measured on the workpiece and the parameter (width value, number of planes, etc.) extracted from the light-receiving waveform, The abnormality type determination unit 132 determines that it is multiple reflection. Furthermore, sometimes the amount of received light decreases (the adjustment parameter of the amount of received light increases), and these feature information can also be used to determine contamination of the sensor.

FIG. 6E is a diagram showing an example of determination (e) mutual interference. As shown in FIG. 6E , in the presence of mutual interference, the number of planes increases. For example, if the interfering light flickers periodically, the peak waveform does not change, and the number of planes changes repeatedly (short-time sudden change). In this case, the abnormality type determination unit 132 determines that there is mutual interference. Furthermore, sometimes the amount of received light and the adjustment parameters of the received light amount change, and the background level also changes, and these feature information can also be used for determination of mutual interference.

FIG. 6F is a diagram showing an example of determination (f) disturbance light. As shown in FIG. 6F , in the presence of interfering light, the amount of received light, the adjustment parameter of the received light amount, the width value, the total area of the received light waveform, and the background level change (short-term sudden change). In particular, the background level increases but sometimes decreases, which is different from (a) sensor contamination mentioned above in this point. Furthermore, when the displacement sensor 10 exceeds the range of a reference value (threshold value) that can appropriately measure a workpiece, the abnormality type determination unit 132 determines that it is disturbance light.

In addition, the determination of the abnormal type is not limited to the conditions described here. For example, as long as the abnormal type can be determined outside the conditions described here, other conditions can also be used for determination. Regarding the combination of feature information, as long as it can If the abnormal type is judged outside of the combinations described here, other combinations can also be used for judgment.

In addition, here, the abnormality type determination unit 132 determines the abnormality types (a) to (f) using the monitoring methods (A) to (C) based on the characteristic information of (1) to (6), but does not limited to these. For example, the abnormal category may also include penetration, head tilt, and transparent object detection, and the feature information may include information related to the tilt or center of gravity of the light-receiving waveform, or other feature information that can be extracted from the light-receiving waveform. Furthermore, as a monitoring method, for example, a method of continuously monitoring, a method of intermittently or periodically (long-term and short-term) monitoring, and the like can also be used. In addition, including the above-mentioned abnormality type, feature information, and monitoring method, it is not necessary to apply all of them, and can be appropriately selected according to the abnormality type, accuracy, and performance desired by the user so that the abnormality type can be appropriately determined.

As described above, the abnormality type is determined by the abnormality type determination unit 132 .

The output unit 133 outputs the abnormality type determined by the abnormality type determination unit 132 . For example, the output unit 133 may output the abnormality category by displaying it on the display unit 150, or may output it by notifying a PC or PLC connected as an external device.

FIG. 7 is a diagram showing an example of abnormality categories displayed on the display unit 150 provided in the amplifier unit. As shown in Fig. 7, the abnormal category "sensor contamination" is displayed. With this, the user can recognize sensor contamination and take countermeasures.

In addition, the content displayed on the display unit 150 is not limited to the type of abnormality, and countermeasures or estimated causes corresponding to the type of abnormality such as "Please check the contamination of the sensor." or "Please replace the light source." may also be displayed. Thereby, the user can directly and concretely grasp what kind of countermeasures should be taken.

FIG. 8 is a diagram showing an example of error codes and countermeasures associated with abnormality types. For example, a correspondence table as shown in FIG. 8 is pre-stored in the storage unit 140, and the output unit 133 can refer to the correspondence table based on the abnormality category determined by the abnormality category determination unit 132, and output the abnormality category, error code, and countermeasure. Any one or more of the policies.

In addition, when the display screen of the display unit 150 is small, if an error code associated with an abnormality type is displayed, the user, for example, can refer to a pre-prepared error code, an abnormality type, and a countermeasure. Create a corresponding table and grasp what countermeasures should be taken according to the displayed error codes.

Furthermore, the content output by the output unit 133 and the content displayed by the display unit 150 can also be switched according to the situation of the regular point detection displacement sensor 10 and the situation of the actual measurement of the workpiece W. For example, in the case of determining an abnormal type that affects the measurement result during the operation of the measurement workpiece W, the output unit 133 outputs a warning (Warning) signal, and replaces the measurement result with an indeterminate value or an error, and furthermore, the The meaning is displayed on the display unit 150 . In addition, when detailed information on the type of abnormality is required, continuous time-series data, waveforms, and the like can be displayed based on data of received light waveforms or characteristic information stored in the memory unit 140 .

[Status monitoring method] Next, a state monitoring method for monitoring the state of the displacement sensor 10 and its surrounding environment including determination of the type of abnormality will be described.

FIG. 9 is a flowchart showing a process flow of a state monitoring method M10 for monitoring the state of the displacement sensor 10 and its surrounding environment including abnormality type determination while the displacement sensor 10 measures the workpiece W. As shown in FIG. 9 , the state monitoring method M10 includes steps S11 to S19 , and each step is executed by a processor included in the displacement sensor 10 .

In step S11, as described using FIG. 1 , the displacement sensor 10 projects the laser light L1 on the workpiece W, and receives the reflected light L2 from the workpiece W relative to the laser light L1, thereby measuring the distance of the surface of the workpiece W. displacement.

In step S12 , the light reflected by the workpiece W (inspection region) is received by the light receiving unit 120 , and a light-receiving waveform is output.

In step S13 , the feature information extraction unit 131 in the control unit 130 extracts at least two or more types of feature information based on the received light waveform output in step S12 . As a specific example, extract the (1) received light amount, (2) light received amount adjustment parameter, (3) width value, (4) number of planes, (5) total area of received light waveform, and (6) background position explained with reference to FIG. 4 . Accurate as feature information.

In step S14 , the feature information extracted in step S13 is stored in the storage unit 140 by the control unit 130 .

In step S15 to step S18, the abnormality category is judged by the abnormality category determination unit 132 in the control unit 130 based on the feature information stored in step S14 and referring to the abnormality category information pre-stored in the memory. As a specific example, as described using FIG. 5 , the abnormality type determination unit 132 determines (1) received light amount, (2) light received amount adjustment parameter, (3) width value, (4) surface number, (5) received light waveform total Area, and (6) background level of these six characteristic information, using the three monitoring methods of (A) reference value difference, (B) short-term sudden change, and (C) light waveform at a certain time point, to determine ( There are six types of anomalies: a) sensor contamination, (b) light source degradation, (c) narrow area measurement, (d) multiple reflections, (e) mutual interference, and (f) interference light.

In step S19 , the abnormality types determined in steps S15 to S18 are output by the output unit 133 in the control unit 130 . As a specific example, the abnormality category is displayed on the display unit 150 .

In addition, a series of processes related to the determination of the abnormality type in step S13 to step S19 may be executed in each measurement cycle, or may be appropriately executed as needed.

FIG. 10 is a flowchart showing a specific processing flow of the abnormality type determination method M100 executed in steps S15 to S18 in FIG. 9 . As shown in FIG. 10 , the abnormal type determination method M100 includes steps S101 to S116 , and each step is executed by a processor included in the displacement sensor 10 .

Here, as processing in each step, the abnormality type determination unit 132 monitors whether each characteristic information changes (rises or falls), that is, changes in a short time, and is within the range (over or below) of the reference value. Determine the exception category. The processing in these steps will be specifically described.

In step S101, if (1) the received light amount is lower than the initial reference value, or (2) the light received amount adjustment parameter exceeds the initial reference value (Yes (Yes) in step S101), the abnormality type determination unit 132 determines that (b) the light source degradation (step S102).

Here, the so-called initial reference value is set based on, for example, the value detected by teaching when the displacement sensor 10 is purchased and used for the first time. This is used to grasp the amount of change (difference) from when the displacement sensor 10 was first used. Regarding the received light amount and the light received amount adjustment parameter, the initial reference value can be set within a range in which the displacement sensor 10 can properly measure the workpiece.

Also, if the displacement sensor 10 is purchased and used for the first time without teaching and without setting an initial reference value, the abnormality type determination unit 132 may not be able to determine (b) light source degradation. In this case, the abnormality type determination unit 132 can also determine (b) light source degradation by using a value set in advance in the displacement sensor 10 or setting by the user.

In step S103 (No (No) in step S101 ), the abnormality type determination unit 132 determines whether (4) the face count has increased and exceeded the reference value.

Here, the reference value is set based on, for example, a value detected by teaching when the displacement sensor 10 is started to operate. The amount of change (difference) from when the device 10 starts working. Regarding this feature information (including feature information described below), the reference value can be set within a range in which the displacement sensor 10 can properly measure the workpiece. In addition, the reference value can also be updated every time the teaching is performed. Assuming that the teaching is not performed at the start of the operation of the displacement sensor 10, the value preset in the displacement sensor 10 can also be used. or set by the user.

In step S104 (Yes in step S103 ), if (4) the face count does not decrease and is not within the reference value range (No in step S104 ), the abnormality type determination unit 132 determines (d) multiple reflection (step S105 ) .

In step S106 (YES in step S104 ), if (4) the face count repeatedly increases and decreases (YES in step S106 ), the abnormality type determination unit 132 determines (e) mutual interference (step S107 ).

In step S108 (No in step S103 ), the abnormality type determination unit 132 determines whether (1) the received light amount falls below the reference value, or (2) the received light amount adjustment parameter increases and exceeds the reference value.

In step S109 (YES in step S108 ), if (3) the width value increases (YES in step S109 ), the abnormality type determination unit 132 determines (a) sensor contamination (step S110 ).

In step S111 (No in step S109 ), if (1) the received light amount increases, and (2) the light received amount adjustment parameter decreases (Yes in step S111 ), the abnormality type determination unit 132 determines (c) stenosis measurement (step S112).

In step S113 (No in step S108 ), the abnormality type determination unit 132 determines whether (1) the received light amount has increased and exceeded the reference value, or (2) the received light amount adjustment parameter has decreased and has fallen below the reference value.

In step S114 (YES in step S113 ), the abnormality type determination unit 132 determines whether (5) the total area of the received light waveform has increased and exceeded the reference value, and (6) the background level has increased and exceeded the reference value.

In step S115 (Yes in step S114), if (5) the total area of the light-receiving waveform decreases and is lower than the reference value, (6) the background level decreases and is lower than the reference value (Yes in step S115), the abnormal type determination unit 132 judged as interference light (step S116)

In this way, the abnormality type determination unit 132 determines six of (a) sensor contamination, (b) light source deterioration, (c) narrow portion measurement, (d) multiple reflection, (e) mutual interference, and (f) interference light exception class.

As described above, according to the displacement sensor 10 and the state monitoring method M10 according to an embodiment of the present invention, the characteristic information extraction unit 131 extracts a plurality of characteristic information from the received light waveform, and the abnormality type determination unit 132 based on the plurality of characteristic information, It is possible to determine at least one abnormality type among a plurality of abnormality types in the abnormality type information stored in the storage unit 140 in advance. As a result, the type of abnormality can be determined appropriately, and the user can take a countermeasure corresponding to the type of abnormality.

In addition, in this embodiment, a case was described in which the abnormality types shown in (a) to (f) in FIG. 5 are determined, but the abnormality types that can be determined may differ depending on the state of the inspection area.

11 is a diagram showing an example of an abnormality category that cannot be determined based on the presence or absence of a workpiece and a base in an inspection area, among the abnormality categories in FIG. 5 . As shown in Fig. 11, regarding the abnormality types (a) to (f), it can be properly judged when there is a workpiece in the inspection area, but when there are no workpieces and bases in the inspection area, regarding (a ) sensor contamination, (c) stenosis measurement, and (d) multiple reflections, sometimes cannot be properly judged.

In this way, depending on the conditions of the inspection area, there are abnormality types that can be appropriately determined and abnormal types that cannot be appropriately determined. Therefore, such information may be included in the abnormality type information stored in the memory unit 140 in advance, for example.

In this way, for example, in the case where the abnormality type determination unit 132 determines (c) measurement of a stenosis, by displaying a message on the display unit 150 as “When there is no workpiece, it may not be possible to properly determine , or a notification similar to it, may also allow the user to confirm. In addition, the abnormality category to be judged, the conditions that can be appropriately judged for each abnormality category, etc. may be displayed on the display unit 150 in advance so that the user can confirm it so that the abnormality category desired by the user can be appropriately judged.

Furthermore, the user can also input a time zone that satisfies an appropriate condition (for example, the existence of a workpiece and a base in the present embodiment) through the operation unit 160 . It can be seen from this that the abnormality type determined in this time period is a result of appropriate determination.

In addition, in this embodiment, as the abnormality type information stored in the storage unit 140, as shown in FIG. The table is an example, but is not limited thereto. For example, a corresponding table may be created for each monitoring method with feature information and abnormality types.

FIG. 12 is a diagram showing an example of abnormality type information in which used feature information and predicted abnormality types are associated with each monitoring method. As shown in Fig. 12, in the monitoring methods (A) to (C), it is possible to grasp which of the characteristic information of (1) to (6) is monitored and to determine the abnormality type of (a) to (f) . Here, there are cases where the abnormality type can be uniquely determined by a combination of the monitoring method and characteristic information, and further abnormality types can be determined by a plurality of combinations of these.

In addition, in the case of using the reference value difference as a monitoring method, it is necessary to set a reference value in advance. Therefore, even if the reference value is not set in advance, it may be possible to determine the type of abnormality.

In this way, the form in which the abnormality type information is used as the abnormality type information stored in the storage unit 140 depends on the type of the displacement sensor 10, the abnormality type desired by the user, and the specific determination in the abnormality type determination unit 132. The order of processing and the like may be adopted. In addition, various forms of abnormality type information can be stored in the storage unit 140, and abnormality type information can also be stored in a form other than the abnormality type information shown in FIGS. 5 and 12 .

The embodiments described above are for facilitating the understanding of the present invention, and are not intended to limit the interpretation of the present invention. Each member included in the embodiment, its arrangement, material, condition, shape, size, etc. are not limited to the illustrated ones, and can be appropriately changed. In addition, the configurations shown in different embodiments can be partially substituted or combined.

[Note] A displacement sensor (10), comprising: a light projecting part (110), projecting light to the inspection area; a light receiving unit (120), receiving the light reflected by the inspection area, and outputting a light receiving waveform; The memory unit (140) pre-memorizes abnormal category information corresponding to a plurality of characteristic information contained in the light receiving waveform and a plurality of abnormal categories predicted according to the plurality of characteristic information; A feature information extraction unit (131), extracting at least two or more types of feature information based on the light receiving waveform output by the light receiving unit; An abnormal category determination unit (132), based on the extracted at least two or more types of feature information, determines at least one abnormal category among the plurality of abnormal categories in the abnormal category information pre-stored in the memory unit; and An output unit (133) outputs the determined abnormality type.

10: Displacement sensor 11: cable 100: sensor head 110: Projector 111: Light emitting element 112: Light projection control circuit 120: Light receiving part 121: Camera element 122: Signal processing circuit 123: A/D conversion circuit 130: Control Department 131: Feature Information Extraction Department 132: Abnormal Category Judgment Department 133: output unit 140: memory department 150: display part 160: Operation Department 170: Input and output interface L1: laser light L2: reflected light B: base M10: Status Monitoring Method M100: Abnormal Category Judgment Method S11～S19: Each step of the state monitoring method M10 S101-S116: Each step of the abnormality category determination method M100 W: Workpiece

FIG. 1 is a perspective view showing the appearance of a sensor head of a displacement sensor 10 according to an embodiment of the present invention. FIG. 2 is a block diagram showing functions constituting the displacement sensor 10 according to one embodiment of the present invention. FIG. 3 is a block diagram showing the configuration of the control unit 130 related to the determination process of the abnormality type and the related functions in the displacement sensor 10 according to the embodiment of the present invention. FIG. 4 is a diagram showing an example of feature information extracted from a light-receiving waveform. FIG. 5 is a diagram showing an example of abnormality type information in which characteristic information is associated with an abnormality type predicted from the characteristic information. FIG. 6A is a diagram showing an example of determination (a) sensor contamination. FIG. 6B is a diagram showing an example of determination (b) of light source degradation. FIG. 6C is a diagram showing an example of determination (c) stenosis measurement. FIG. 6D is a diagram showing an example of determination (d) of multiple reflections. FIG. 6E is a diagram showing an example of determination (e) mutual interference. FIG. 6F is a diagram showing an example of determination (f) disturbance light. FIG. 7 is a diagram showing an example of abnormality categories displayed on a display unit provided in the amplifier unit. FIG. 8 is a diagram showing an example of error codes and countermeasures associated with abnormality types. FIG. 9 is a flowchart showing a process flow of a state monitoring method M10 for monitoring the state of the displacement sensor 10 and its surrounding environment including abnormality type determination while the displacement sensor 10 is measuring the workpiece W. FIG. 10 is a flowchart showing a specific processing flow of the abnormality type determination method M100 executed in steps S15 to S18 in FIG. 9 . 11 is a diagram showing an example of an abnormality category that cannot be determined based on the presence or absence of a workpiece and a base in an inspection area, among the abnormality categories in FIG. 5 . FIG. 12 is a diagram showing an example of abnormality type information in which used feature information and predicted abnormality types are associated for each monitoring method.

10: Displacement sensor

110: Projector

111: Light emitting element

112: Light projection control circuit

120: Light receiving part

121: Camera element

122: Signal processing circuit

123: A/D conversion circuit

130: Control Department

140: memory department

150: display part

160: Operation Department

170: Input and output interface

Claims (9)

A displacement sensor comprising: The light projecting part projects light to the inspection area; a light receiving part, receiving the light reflected by the inspection area, and outputting a light receiving waveform; The memory unit pre-stores abnormal category information, and the abnormal category information establishes a correspondence between a plurality of characteristic information contained in the light receiving waveform and a plurality of abnormal categories predicted according to the plurality of characteristic information; The feature information extraction unit extracts at least two or more types of feature information based on the light receiving waveform output by the light receiving unit; The abnormality category determination unit determines at least one abnormality category among the plurality of abnormality categories in the abnormality category information pre-stored in the storage unit based on the extracted at least two kinds of feature information; and The output unit outputs the determined abnormality type. The displacement sensor as claimed in item 1, wherein The abnormality type determination unit uses a monitoring method of at least one of information contained in a difference amount from a reference value, a change amount within a predetermined period, and a light reception waveform at a certain point in time for the extracted characteristic information. To determine the abnormal category. The displacement sensor as described in claim 2, wherein Among the plurality of abnormality types in the abnormality type information stored in the storage unit in advance, the abnormality types that can be determined according to the monitoring method are classified. The displacement sensor according to any one of claim 1 to claim 3, wherein The feature information includes at least two of the received light amount, the light received amount adjustment parameter, the width value of the light received waveform, the number of planes of the light received waveform, the total area of the light received waveform, and the background level. The displacement sensor as described in claim item 4, wherein The abnormality type determination unit is based on the amount of received light, the adjustment parameter of the received light amount, the width value of the received light waveform, the number of planes of the received light waveform, the total area of the received light waveform, and the background level. Combinations of at least two or more are used to determine the abnormal category. The displacement sensor as described in claim 5, wherein Among the plurality of abnormality categories in the abnormality category information pre-stored in the memory unit, the received light quantity, the light received quantity adjustment parameter, the width value of the light received waveform, the number of planes of the light received waveform, The total area of the light-receiving waveform and the abnormality category determined in combination of at least two or more of the background levels are classified. The displacement sensor according to any one of claim 1 to claim 3, wherein Among the plurality of abnormality categories in the abnormality category information pre-stored in the memory unit, the abnormality categories that can be determined based on the presence or absence of workpieces and bases in the inspection area are classified. The displacement sensor according to any one of claim 1 to claim 3, wherein The output unit outputs a countermeasure or an estimated cause corresponding to the determined abnormality type. A state monitoring method is a state monitoring method performed by a displacement sensor including a processor, comprising: a light projecting step, projecting light to the inspection area; a light receiving step, receiving light reflected by the inspection area, and outputting a light receiving waveform; The feature information extraction step is to extract at least two or more feature information based on the light receiving waveform output in the light receiving step; The abnormal category determination step is to determine at least one abnormal category among the plurality of abnormal categories in the abnormal category information pre-stored in the memory based on the extracted at least two or more feature information, the abnormal category information Corresponding a plurality of characteristic information included in the light receiving waveform with a plurality of abnormal categories predicted according to the plurality of characteristic information; and The output step is to output the determined abnormality category.

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