Displacement sensor and state monitoring method

Abstract

The present application provides a displacement sensor and a state monitoring method capable of properly determining an abnormality category. The displacement sensor (10) has: a light projecting section (110) that projects light to an inspection region; a light receiving section (120) that receives light reflected by the inspection region and outputs a light receiving waveform; a storage section (140) that stores in advance abnormality category information obtained by associating a plurality of characteristic information included in the light receiving waveform with a plurality of abnormality categories predicted from the plurality of characteristic information; a characteristic information extraction section (131) that extracts at least two or more kinds of characteristic information based on the light receiving waveform output by the light receiving section; an abnormality category determination section (132) that determines at least one or more kinds of abnormality categories among the plurality of abnormality categories of the abnormality category information stored in advance in the storage section based on the extracted at least two or more kinds of characteristic information; and an output section (133) that outputs the determined abnormality category.

Description

Technical Field

[0001] This invention relates to displacement sensors and state monitoring methods. Background Technology

[0002] Previously, displacement sensors utilizing optical systems were used as devices for measuring workpiece displacement in a non-contact manner. However, such displacement sensors may fail to measure the workpiece properly due to factors such as sensor aging, dirt accumulation caused by dust or other foreign matter, and influences from the surrounding environment.

[0003] Patent Document 1 discloses a technology related to a detection device that detects abnormal signs in a detection sensor before the sensor malfunctions. Specifically, the detection device disclosed in Patent Document 1 acquires the amount of light received in the light-receiving section when a workpiece passes through the detection position at each predetermined cycle, and generates a light-receiving waveform based on the acquired result. Furthermore, the detection device determines whether there are abnormal signs in the detection sensor by comparing a reference waveform and the light-receiving waveform.

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 2012-78175 Summary of the Invention

[0007] The problem that the invention aims to solve

[0008] However, in the detection device disclosed in Patent Document 1, although it determines whether there are abnormal signs in the detection sensor, it cannot identify the cause or details even if abnormal signs are detected. Therefore, users may not be able to determine what specific countermeasures to take afterward.

[0009] Therefore, the purpose of this invention is to provide a displacement sensor and a state monitoring method that can appropriately determine the type of anomaly.

[0010] Methods for solving problems

[0011] One aspect of the present invention provides a displacement sensor comprising: a light-projecting unit that projects light onto an inspection area; a light-receiving unit that receives light reflected from the inspection area and outputs a light-receiving waveform; a storage unit that pre-stores anomaly category information, the anomaly category information being obtained by mapping multiple feature information contained in the light-receiving waveform to multiple anomaly categories predicted based on the multiple feature information; a feature information extraction unit that extracts at least two or more feature information based on the light-receiving waveform output by the light-receiving unit; an anomaly category determination unit that determines at least one or more anomaly categories from the multiple anomaly categories pre-stored in the anomaly category information in the storage unit based on the extracted at least two or more feature information; and an output unit that outputs the determined anomaly category. Here, the light-receiving waveform is generated as a waveform representing the amount of light received by the imaging element of the light-receiving unit, with each pixel representing its light-receiving amount.

[0012] According to this method, the feature information extraction unit extracts at least two feature information based on the light-receiving waveform, and the anomaly category determination unit can appropriately determine at least one of the multiple anomaly categories of anomaly category information pre-stored in the storage unit based on the feature information.

[0013] In the above method, the anomaly category determination unit can determine the anomaly category based on the extracted feature information by using at least one of the following monitoring methods: the difference from the reference value, the amount of change within a specified period, and the information contained in the light-receiving waveform at a certain point in time.

[0014] According to this method, the anomaly category determination department uses multiple monitoring methods to determine the anomaly category, thus enabling more appropriate anomaly category determination.

[0015] In the above method, it is also possible to classify the abnormal categories that can be determined by the monitoring method from among multiple abnormal category information stored in the storage unit in advance.

[0016] According to this method, the anomaly category information is classified into anomaly categories that can be determined by the monitoring method. Therefore, in the determination of anomaly categories, an appropriate monitoring method can be used according to each anomaly category.

[0017] In the above method, the feature information may include at least two of the following: light intensity, light intensity adjustment parameters, width value of the light-receiving waveform, number of surfaces of the light-receiving waveform, total area of ​​the light-receiving waveform, and background level.

[0018] According to this method, the feature information extracted from the light-receiving waveform includes at least two of the following: light amount, light amount adjustment parameters, width value of the light-receiving waveform, number of surfaces of the light-receiving waveform, total area of ​​the light-receiving waveform, and background level. Therefore, it is possible to determine the anomaly category more specifically based on these.

[0019] In the above method, the anomaly category determination unit can determine the anomaly category based on at least two or more combinations of light received amount, light received amount adjustment parameters, width value of light received waveform, number of surfaces of light received waveform, total area of ​​light received waveform, and background level.

[0020] According to this method, the anomaly category determination unit appropriately combines multiple feature information to determine each anomaly category, thus enabling more appropriate anomaly category determination.

[0021] In the above method, it is also possible to classify anomalies based on at least two or more combinations of light-receiving amount, light-receiving amount adjustment parameters, width value of light-receiving waveform, number of surfaces of light-receiving waveform, total area of ​​light-receiving waveform, and background level from among multiple anomaly categories pre-stored in the storage unit.

[0022] According to this method, in the anomaly category information, anomalies are classified into categories that can be determined by at least two combinations of light received amount, light received amount adjustment parameters, width value of light received waveform, number of surfaces of light received waveform, total area of ​​light received waveform, and background level. Therefore, in the determination of anomalies, feature information can be appropriately combined according to each anomaly category.

[0023] In the above method, among multiple anomaly categories pre-stored in the storage unit, anomalies that can be determined based on the presence or absence of a workpiece and a base in the inspection area are categorized. Furthermore, the workpiece refers to the object being measured by the displacement sensor, and the base refers to the platform used to hold the workpiece.

[0024] According to this method, the type of abnormality can be appropriately determined based on the presence or absence of workpieces and bases in the inspection area.

[0025] In the above method, the output unit can output the countermeasures or presumed causes corresponding to the determined anomaly category.

[0026] This method allows users to directly and specifically understand what countermeasures should be taken.

[0027] One aspect of the present invention is a state monitoring method executed by a displacement sensor including a processor, comprising: a light projection step, projecting light onto an inspection area; a light receiving step, receiving light reflected from the inspection area and outputting a light-receiving waveform; a feature information extraction step, extracting at least two types of feature information based on the light-receiving waveform output in the light receiving step; an anomaly category determination step, determining at least one anomaly category from multiple anomaly category information pre-stored in a memory, which is obtained by mapping multiple feature information contained in the light-receiving waveform to multiple anomaly categories predicted based on the multiple feature information, based on the extracted at least two types of feature information; and an output step, outputting the determined anomaly category.

[0028] According to this method, at least two feature information is extracted based on the light waveform in the feature information extraction step, and in the anomaly category determination step, at least one anomaly category among multiple anomaly categories of anomaly category information pre-stored in the memory can be appropriately determined based on the feature information.

[0029] The effects of the invention

[0030] According to the present invention, a displacement sensor and a state monitoring method capable of appropriately determining the type of anomaly can be provided. Attached Figure Description

[0031] Figure 1 This is a perspective view showing the appearance of the sensor head of a displacement sensor 10 according to one embodiment of the present invention.

[0032] Figure 2 This is a block diagram illustrating the functions of a displacement sensor 10 constituting one embodiment of the present invention.

[0033] Figure 3 This is a block diagram showing the control unit 130 related to the determination and processing of abnormality categories in a displacement sensor 10 according to one embodiment of the present invention, and the functional structures associated therewith.

[0034] Figure 4 This is a diagram illustrating an example of feature information extracted from the received light waveform.

[0035] Figure 5 This is a diagram illustrating an example of anomaly category information obtained by mapping feature information to anomaly categories predicted based on that feature information.

[0036] Figure 6A This is a diagram illustrating an example of determining (a) sensor contamination.

[0037] Figure 6B This is a diagram illustrating an example of determining (b) light source degradation.

[0038] Figure 6C This is a diagram illustrating an example of measurement for determining (c) a narrow location.

[0039] Figure 6D This is a diagram illustrating an example of determining (d) multiple reflections.

[0040] Figure 6E This is a diagram illustrating an example of mutual interference in the determination (e).

[0041] Figure 6F This is a diagram illustrating an example of determining (f) interfering light.

[0042] Figure 7 This diagram illustrates an example of displaying an abnormality category on the display unit 150 located in the amplifier section.

[0043] Figure 8 This is a diagram illustrating an example of an error code corresponding to an exception category and its corresponding countermeasure.

[0044] Figure 9 This is a flowchart of a state monitoring method M10 that represents the state of the displacement sensor 10 and its surrounding environment, including the determination of abnormality categories.

[0045] Figure 10 It means in Figure 9 The flowchart shows the specific processing flow of the exception category determination method M100 executed in steps S15 to S18.

[0046] Figure 11 It means Figure 5 The figure shows an example of an anomaly category that cannot be determined based on the presence or absence of a workpiece and base in the inspection area.

[0047] Figure 12 This is a diagram illustrating an example of anomaly category information obtained by mapping the feature information used to the predicted anomaly category for each monitoring method. Detailed Implementation

[0048] Hereinafter, embodiments of the present invention will be specifically described with reference to the accompanying drawings. Furthermore, the embodiments described below are merely specific examples for implementing the present invention and are not intended to limit the scope of the invention. Additionally, for ease of understanding, the same reference numerals will be used as much as possible to denote the same constituent elements in the drawings, and sometimes repeated descriptions will be omitted.

[0049] <One implementation method>

[0050] [Structure of the displacement sensor]

[0051] Figure 1This is a perspective view showing the appearance of the sensor head of a displacement sensor 10 according to one embodiment of the present invention. The displacement sensor 10 has a sensor head 100 and an auxiliary housing (not shown) called an amplifier section connected via a cable 11.

[0052] like Figure 1 As shown, the displacement sensor 10 projects a laser L1 from the sensor head 100 onto the workpiece W placed on the base B, and receives the reflected light L2 from the workpiece W relative to the laser L1, thereby measuring the displacement of the surface of the workpiece W based on the principle of triangulation. Furthermore, the measured displacement is the distance from the sensor head 100 to the surface of the workpiece W, and the distance can be output as detection data.

[0053] Thus, the displacement sensor 10 is usually a measuring device that projects a laser onto the workpiece W to measure the displacement of the surface of the workpiece W. However, in this embodiment, for example, in the absence of the workpiece W, the assembly robot or other equipment and the surrounding environment can be used as the inspection area to project the laser and receive the reflected light from the inspection area, thereby performing status monitoring.

[0054] Figure 2 This is a block diagram illustrating the functions of a displacement sensor 10 constituting one embodiment of the present invention. For example... Figure 2 As shown, the displacement sensor 10 includes a light-emitting unit 110, a light-receiving unit 120, a control unit 130, a storage unit 140, a display unit 150, an operation unit 160, and an input / output interface 170. Furthermore, the light-emitting unit 110 includes a light-emitting element 111 and a light-emitting control circuit 112, while the light-receiving unit 120 includes an imaging element 121, a signal processing circuit 122, and an A / D conversion circuit 123.

[0055] For example, the light-emitting unit 110, the light-receiving unit 120, the control unit 130, and the storage unit 140 are assembled in Figure 1 The sensor head 100, display unit 150, operation unit 160, and input / output interface 170 shown are disposed in the amplifier unit. However, the sensor head 100 and the amplifier unit do not necessarily need to be separated; they can also be combined. Figure 2 All the structures shown are housed in a single housing.

[0056] The projection unit 110 projects light onto the inspection area (e.g., workpiece W). Specifically, the projection unit 110 has a laser diode (LD) as a light-emitting element 111, and drives the light-emitting element 111 to project light while adjusting the light intensity and light emission time of the light-emitting element 111 by the projection control circuit 112. In addition, the projection control circuit 112 operates based on instructions from the control unit 130.

[0057] The light-receiving unit 120 receives light reflected from the inspection area and outputs a light-receiving waveform. Specifically, the light-receiving unit 120 has a CMOS sensor containing multiple pixels as an imaging element 121, and also has a signal processing circuit 122 and an A / D conversion circuit 123 for processing the image signal generated by the imaging element 121. The signal processing circuit 122 controls the timing of the operation of the imaging element 121 according to the instructions from the control unit 130, and captures the image generated by the imaging element 121 and outputs it to the A / D conversion circuit 123. Then, the image after analog-to-digital conversion by the A / D conversion circuit 123 is input to the control unit 130 for measurement processing.

[0058] The control unit 130, for example a CPU, emits laser L1 from the projection unit 110 based on programs and setting data stored in the storage unit 140, and activates the light-receiving unit 120 in a timing match to receive reflected light L2 from the workpiece W. Then, the control unit 130 measures the displacement of the workpiece W through various processing based on the light-receiving waveform output by the light-receiving unit 120.

[0059] Furthermore, the control unit 130 extracts feature information contained in the light-receiving waveform output by the light-receiving unit 120, and determines the anomaly category inferred based on the extracted feature information. Details regarding the anomaly category determination process in the displacement sensor 10 will be described later.

[0060] The storage unit 140 is, for example, a non-volatile memory such as an EEPROM, storing programs, setting data for defining various actions controlled by the control unit 130, and exception category information used in the exception category determination process described later. Additionally, a buffer function is configured for accumulating data such as the light-receiving waveform output by the light-receiving unit 120 and feature information extracted from that waveform, used in the exception category determination process described later.

[0061] The display unit 150 is configured, for example, by a liquid crystal display or an organic EL display, and displays the measured values ​​from the control unit 130, the types of abnormalities described later, the countermeasures corresponding to the types of abnormalities, and other information that should be notified to the user, such as settings and statuses related to the displacement sensor 10.

[0062] The operation unit 160 consists of buttons, dials, or touch panels, for example, to enable the user to turn the power supply of the displacement sensor 10 on and off, set various parameters, and switch operating modes.

[0063] The input / output interface 170 can be connected to external devices such as a PC (personal computer) and a PLC (programmable logic controller). When connected to an external device, the same operations performed in the operation unit 160 can be performed through the external device. For example, operations on the displacement sensor 10 can be performed on the PC, or the measurement results obtained by the displacement sensor 10 can be displayed on the PC screen.

[0064] [Abnormality Class Determination and Handling]

[0065] Next, the anomaly category determination processing performed by the displacement sensor 10 will be described in detail. Here, the anomaly category determination processing refers to, for example, the function of detecting anomalies or signs of anomalies in the workpiece W, such as aging of the displacement sensor 10, dirt caused by the adhesion of foreign matter such as dust, and influences caused by the surrounding environment.

[0066] Figure 3 This is a block diagram illustrating the control unit 130 related to anomaly category determination processing in a displacement sensor 10 according to one embodiment of the present invention, and the associated functional structures. For example... Figure 3 As shown, the control unit 130 has a feature information extraction unit 131, an anomaly category determination unit 132, and an output unit 133. While referring to the storage unit 140, it determines the anomaly category and outputs it to the display unit 150.

[0067] The feature information extraction unit 131 extracts at least two 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 multiple feature information (described later) based on the light-receiving waveform continuously output by the light-receiving unit 120 and stores them in the storage unit 140.

[0068] Figure 4 This is a diagram illustrating an example of feature information extracted from the received light waveform. In the diagram, pixel position represents the position in the baseline direction of triangulation of pixels on the imaging element 121, and LSB represents the average amount of light received by the pixel corresponding to that pixel position. Figure 4 As shown, the following parameters are extracted from the received waveform: (1) amount of light received, (2) light received adjustment parameters, (3) width value, (4) number of surfaces, (5) total area of ​​the received waveform, and (6) background level.

[0069] In addition, (1) the amount of light received refers to the amount of light received by the light-receiving section 120; (2) the amount of light received adjustment parameter refers to the adjustment required for the output of the light-receiving waveform based on the amount of light received by the light-receiving section 120 (e.g., gain and exposure time, etc.); (3) the width value is, for example, the width of the light-receiving waveform at 50% of the peak amount of light received (half-value width), representing the width of the light-receiving waveform; (4) the number of surfaces is the number of peaks in the light-receiving waveform, representing the number of light components; (5) the total area of ​​the light-receiving waveform is, for example, Figure 4 The total area occupied by the light-receiving waveform shown, (6) the background level indicates the amount of ambient light received by the light-receiving unit 120 when the displacement sensor 10 does not project the light for measurement. In addition, the width value is not limited to the width of the light-receiving waveform at 50% of the peak light-receiving amount (half-value width), as long as it represents the width of the light-receiving waveform, for example, it can also be the width value of the light-receiving waveform at 40%, 60%, etc. of the peak light-receiving amount.

[0070] Thus, in this embodiment, the feature information extraction unit 131 extracts the above-mentioned six feature information (1) to (6) from the light-receiving waveform output by the light-receiving unit 120.

[0071] The anomaly category determination unit 132 determines at least one of the multiple anomaly categories from the multiple anomaly categories stored in the storage unit 140 based on at least two or more feature information extracted by the feature information extraction unit 131. Specifically, the anomaly category determination unit 132 determines the anomaly category based on the feature information (1) to (6) described above, by their time series changes and their combinations. Here, the storage unit 140 stores anomaly category information obtained by corresponding multiple feature information contained in the light-receiving waveform with multiple anomaly categories predicted based on the multiple feature information. The anomaly category determination unit 132 determines the anomaly category while referring to the anomaly category information stored in the storage unit 140 based on the feature information (1) to (6) described above.

[0072] Figure 5 This is a diagram illustrating an example of anomaly category information obtained by mapping feature information to anomaly categories predicted based on that feature information. For example... Figure 5 As shown, based on the above (1) light received amount, (2) light received amount adjustment parameters, (3) width value, (4) number of surfaces, (5) total area of ​​light received waveform and (6) background level value, time series changes and their combinations, the following six abnormal categories are determined: (a) sensor dirt, (b) light source deterioration, (c) measurement in narrow places, (d) multiple reflections, (e) mutual interference and (f) interference light.

[0073] Furthermore, when the anomaly category determination unit 132 determines the anomaly categories (a) to (f) based on the feature information (1) to (6) described above, the determination is made regarding the monitoring method to be used. The monitoring method may include, for example, a method that monitors (A) the difference between the reference value and the reference value, (B) the amount of change within a specified period, and (C) the information contained in the light-receiving waveform at a certain point in time, based on the feature information (1) to (6) described above. At least one of these monitoring methods is used.

[0074] (A) The difference from the reference value refers to comparing the initial value of the displacement sensor 10 (e.g., the value at the time of manufacture, purchase, or start of operation) with the reference value (threshold). (B) The amount of change within a specified period refers to the amount of change monitored within a short period of a few milliseconds to several hundred milliseconds. (C) The information contained in the light-receiving waveform at a certain point in time refers to the information contained in the temporary waveform in the light-receiving waveform.

[0075] The following provides detailed explanations of specific examples for the exception categories (a) to (f).

[0076] Figure 6A This is a diagram illustrating an example of determining (a) sensor contamination. For example... Figure 6A As shown, in the presence of sensor contamination, the amount of light received, the light received adjustment parameter, and the width value change (short-term abrupt change). Furthermore, as the displacement sensor 10, if the range exceeds the reference value (threshold) that allows for appropriate measurement of the workpiece, the anomaly classification unit 132 determines it to be sensor contamination. Additionally, if the received light waveform contains noise components, the total area of ​​the received light waveform and the background level may further change; this characteristic information can also be used to determine sensor contamination.

[0077] Figure 6B This is a diagram illustrating an example of determining (b) light source degradation. For example... Figure 6B As shown, in the event of light source degradation, the displacement sensor 10 becomes a state where the amount of light received decreases, causing the gain of the light received adjustment parameter to increase. For example, when compared with the initial reference value of the displacement sensor 10 (the value detected by teaching during initial use), if it exceeds the range of the reference value (threshold) that enables appropriate measurement of the workpiece, the anomaly category determination unit 132 determines that the light source is degraded.

[0078] Figure 6C This is a diagram illustrating an example of measurement for determining (c) a narrow location. For example... Figure 6CAs shown, in the case of measurement in a confined space, the light intensity of the displacement sensor 10 sometimes becomes unstable, causing the light waveform to fluctuate, or the light intensity decreases, resulting in an increase in the gain of the light intensity adjustment parameter. Here, the difference from the light source degradation described in (b) above is that in the case of measurement in a confined space, for example, when the state of the inspection area (workpiece) changes (deviating from the confined space position), the light intensity and the light intensity adjustment parameter are restored. In other words, in the case of measurement in a confined space, if the change in the light intensity and the light intensity adjustment parameter is temporary, the anomaly classification unit 132 determines it as a measurement in a confined space.

[0079] Figure 6D This is a diagram illustrating an example of determining (d) multiple reflections. For example... Figure 6D As shown, in the presence of multiple reflections, a change occurs, such as an increase in the number of faces (a sudden change in a short time). Furthermore, as the displacement sensor 10, if the range exceeds the reference value (threshold) of the measured value that allows for appropriate measurement of the workpiece and the parameters (width value, number of faces, etc.) extracted from the received light waveform, the anomaly category determination unit 132 determines it as multiple reflections. Also, sometimes the amount of light received decreases (the light received adjustment parameter increases), and this characteristic information can also be used to determine sensor contamination.

[0080] Figure 6E This is a diagram illustrating an example of mutual interference in judgment (e). For example... Figure 6E As shown, in the presence of mutual interference, the number of faces increases. For example, if the interfering light flickers periodically, the peak waveform remains unchanged, and the number of faces changes repeatedly (short-term abrupt changes). In such cases, the anomaly classification unit 132 determines it as mutual interference. Furthermore, sometimes changes in the amount of light received and the light received adjustment parameters, as well as changes in the background level, can also be used to determine mutual interference.

[0081] Figure 6F This is a diagram illustrating an example of determining (f) interfering light. For example... Figure 6F As shown, in the presence of interfering light, the amount of light received, the light received adjustment parameter, the width value, the total area of ​​the light received waveform, and the background level change (short-term abrupt change). In particular, the background level increases but sometimes decreases, which is different from the sensor dirt mentioned in (a) above. Moreover, as the displacement sensor 10, when the range exceeds the reference value (threshold) that enables proper measurement of the workpiece, the anomaly category determination unit 132 determines it as interfering light.

[0082] Furthermore, the determination of anomaly categories is not limited to the conditions described here. For example, if anomaly categories can be determined according to conditions other than those described here, then other conditions can also be used. Regarding combinations of feature information, if anomaly categories can be determined according to combinations other than those described here, then other combinations can also be used.

[0083] Furthermore, here, the anomaly category determination unit 132 uses the monitoring methods (A) to (C) based on the feature information (1) to (6) to determine the anomaly categories (a) to (f), but is not limited to these. For example, the anomaly categories may include infiltration, head tilt, and detection of transparent bodies, etc., 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, regarding the monitoring method, for example, a method of continuous monitoring and a method of intermittent or periodic (long-term and short-term) monitoring may be used. In addition, it is not necessary to use all of the above-mentioned anomaly categories, feature information, and monitoring methods. The appropriate anomaly categories that can be determined can be selected according to the anomaly categories, accuracy, and performance desired by the user.

[0084] As described above, the anomaly category determination unit 132 determines the anomaly category.

[0085] The output unit 133 outputs the abnormality category determined by the abnormality category determination unit 132. For example, the output unit 133 may output the abnormality category by displaying it on the display unit 150, or by notifying a PC or PLC connected as an external device.

[0086] Figure 7 This diagram illustrates an example of displaying an abnormality category on the display unit 150 located in the amplifier section. (See diagram for example.) Figure 7 As shown, an anomaly category, "Sensor Dirt," is displayed. This allows users to identify and address sensor dirt.

[0087] Furthermore, the content displayed on the display unit 150 is not limited to the category of abnormality. For example, it can also display countermeasures or estimated causes corresponding to the category of abnormality, such as "Please check for dirt on the sensor" or "Please replace the light source." This allows the user to directly and specifically understand what countermeasures should be taken.

[0088] Figure 8 This is a diagram illustrating an example of error codes and corresponding countermeasures for an exception category. For example, it could also be... Figure 8 The corresponding table shown is pre-stored in the storage unit 140. The output unit 133 outputs one or more of the exception category, error code, and countermeasure based on the exception category determined by the exception category determination unit 132 and referring to the corresponding table.

[0089] In addition, if the display screen in the display unit 150 is small, and an error code corresponding to the exception category is displayed, the user can, for example, refer to a pre-prepared table that matches error codes with exception categories and response strategies, and determine the appropriate countermeasures based on the displayed error code.

[0090] Furthermore, the content output by the output unit 133 and the content displayed by the display unit 150 can be switched based on factors such as the periodic checks of the displacement sensor 10 and the actual measurement of the workpiece W. For example, if an anomaly affecting the measurement result is determined during the measurement of the workpiece W, the output unit 133 can output a warning signal and replace the measurement result with an uncertain value or error, then display this information on the display unit 150. In addition, when detailed information about the anomaly type is required, continuous time-series data and waveforms can be displayed based on data stored in the storage unit 140, such as light-receiving waveforms or feature information.

[0091] [Status Monitoring Method]

[0092] Next, a state monitoring method for monitoring the state of the displacement sensor 10 and its surrounding environment, including anomaly category determination, will be described.

[0093] Figure 9 This is a flowchart illustrating the processing flow of a state monitoring method M10 that monitors the state of the displacement sensor 10 and its surrounding environment, including anomaly category determination, while the displacement sensor 10 measures the workpiece W. (Example:) Figure 9 As shown, the state monitoring method M10 includes steps S11 to S19, each step of which is executed by the processor included in the displacement sensor 10.

[0094] In step S11, if using Figure 1 As explained, the displacement sensor 10 projects a laser L1 onto the workpiece W and receives reflected light L2 from the workpiece W in response to the laser L1, thereby measuring the amount of displacement on the surface of the workpiece W.

[0095] In step S12, the light receiving unit 120 receives the light reflected by the workpiece W (inspection area) and outputs the light receiving waveform.

[0096] In step S13, the feature information extraction unit 131 in the control unit 130 extracts at least two types of feature information based on the light-receiving waveform output in step S12. As a specific example, the extraction uses... Figure 4 The following parameters are used as feature information: (1) light received amount, (2) light received amount adjustment parameters, (3) width value, (4) number of surfaces, (5) total area of ​​light received waveform, and (6) background level.

[0097] In step S14, the feature information extracted in step S13 is stored in the storage unit 140 by the control unit 130.

[0098] In steps S15 to S18, the anomaly category determination unit 132 in the control unit 130 determines the anomaly category based on the feature information accumulated in step S14, while referring to the anomaly category information pre-stored in the memory. For a specific example, such as using... Figure 5 As explained, the anomaly classification unit 132 uses three monitoring methods (A) reference value difference, (B) short-time abrupt change, and (C) light waveform at a certain time point to determine six anomaly categories: (a) sensor dirt, (b) light source degradation, (c) measurement in a narrow place, (d) multiple reflections, (e) mutual interference, and (f) interference light. Based on the six feature information (1) light amount, (2) light amount adjustment parameter, (3) width value, (4) number of surfaces, (5) total area of ​​light waveform and (6) background level.

[0099] In step S19, the output unit 133 in the control unit 130 outputs the abnormality category determined in steps S15 to S18. As a specific example, the abnormality category is displayed on the display unit 150.

[0100] In addition, the series of processes related to the anomaly category determination in steps S13 to S19 can be performed in each measurement cycle, or as appropriate according to other needs.

[0101] Figure 10 It means in Figure 9 The flowchart shows the specific processing flow of the exception category determination method M100 executed in steps S15 to S18. Figure 10 As shown, the anomaly category determination method M100 includes steps S101 to S116, each step being executed by the processor included in the displacement sensor 10.

[0102] Here, as part of the processing in each step, the anomaly category determination unit 132 determines the anomaly category by monitoring changes (increases or decreases) in each feature information, i.e., short-term abrupt changes, and whether they are within the range of the baseline value (exceeding or falling below). The processing in each of these steps will be explained in detail.

[0103] In step S101, if (1) the amount of light received is lower than the initial reference value, or (2) the amount of light received adjustment parameter exceeds the initial reference value ("Yes" in step S101), then the anomaly category determination unit 132 determines (b) light source degradation (step S102).

[0104] Here, the initial reference value refers to a value set, for example, based on the value detected by teaching when the displacement sensor 10 was purchased and initially used. In other words, it is a value used to determine the amount (difference) of change in (1) the amount of light received and (2) the amount of light received adjustment parameters relative to when the displacement sensor 10 was initially used. Regarding the amount of light received and the amount of light received adjustment parameters, the initial reference value can be set within the range where the displacement sensor 10 can properly measure the workpiece.

[0105] Furthermore, assuming that no initial reference value was set when the displacement sensor 10 was purchased and initially used, the anomaly category determination unit 132 may sometimes be unable to determine (b) light source degradation. In this case, the anomaly category determination unit 132 can determine (b) light source degradation by using a value preset in the displacement sensor 10 or by a value set by the user.

[0106] In step S103 (the "No" in step S101), the anomaly category determination unit 132 determines whether the number of faces (4) has increased and exceeded the reference value.

[0107] Here, the reference value refers, for example, to a value set based on the value detected through teaching, such as when the displacement sensor 10 is started to operate. In other words, it is a value used to understand the amount of change (difference) from the start of operation of the displacement sensor 10 based on this feature information. Regarding this feature information (including the feature information described later), the reference value can be set within the range where the displacement sensor 10 can appropriately measure the workpiece. Furthermore, the reference value can also be updated each time it is taught. If no teaching is performed, such as when the displacement sensor 10 is started to operate, a value preset in the displacement sensor 10 can be used, or it can be set by the user.

[0108] In step S104 (the "yes" of step S103), if the number of faces (4) does not decrease and is not within the reference value range (the "no" of step S104), then the anomaly category determination unit 132 determines it as (d) multiple reflections (step S105).

[0109] In step S106 (the "yes" of step S104), if the number of faces (4) is repeatedly increasing and decreasing (the "yes" of step S106), then the anomaly category determination unit 132 determines that (e) there is mutual interference (step S107).

[0110] In step S108 (the "No" in step S103), the anomaly category determination unit 132 determines whether (1) the light received amount decreases and is lower than the reference value, or (2) the light received amount adjustment parameter increases and exceeds the reference value.

[0111] In step S109 (yes to step S108), if the width value of (3) increases (yes to step S109), the anomaly category determination unit 132 determines that (a) the sensor is dirty (step S110).

[0112] In step S111 (No in step S109), if (1) the amount of light received increases and (2) the amount of light received adjustment parameter decreases (Yes in step S111), then the abnormality category determination unit 132 determines that (c) measurement in a narrow place (step S112).

[0113] In step S113 (the "No" in step S108), the anomaly category determination unit 132 determines whether (1) the light received amount increases and exceeds the reference value, or (2) the light received amount adjustment parameter decreases and is lower than the reference value.

[0114] In step S114 (the "yes" in step S113), the anomaly category determination unit 132 determines whether (5) the total area of ​​the light-receiving waveform increases and exceeds the reference value, or (6) the background level increases and exceeds the reference value.

[0115] In step S115 (the "Yes" of step S114), if (5) the total area of ​​the light-receiving waveform decreases and is lower than the reference value, and (6) the background level decreases and is lower than the reference value (the "Yes" of step S115), then the anomaly category determination unit 132 determines it as interference light (step S116).

[0116] Thus, the anomaly category determination unit 132 determines six anomaly categories: (a) sensor dirt, (b) light source degradation, (c) measurement in a confined space, (d) multiple reflections, (e) mutual interference, and (f) interference light.

[0117] As described above, in the displacement sensor 10 and state monitoring method M10 according to one embodiment of the present invention, the feature information extraction unit 131 extracts multiple feature information from the received light waveform, and the anomaly category determination unit 132 can determine at least one or more anomaly categories among multiple anomaly categories stored in the storage unit 140 in advance based on the multiple feature information. As a result, the anomaly category can be appropriately determined, and the user can take countermeasures corresponding to the anomaly category.

[0118] Furthermore, in this embodiment, the method for... Figure 5 The cases shown in (a) to (f) are used to determine the anomaly categories, but the anomaly categories that can be determined may vary depending on the state of the inspected area.

[0119] Figure 11 It means Figure 5 This is an example of an anomaly category that cannot be determined based on the presence or absence of a workpiece and base in the inspection area. (See diagram.) Figure 11As shown, regarding the anomaly categories (a) to (f), when a workpiece is present in the inspection area, it is possible to make an appropriate determination. However, when neither a workpiece nor a base is present in the inspection area, it is sometimes impossible to make an appropriate determination for (a) sensor dirt, (c) measurement in a confined space, and (d) multiple reflections.

[0120] Thus, depending on the conditions of the inspection area, there are abnormality categories that can be appropriately determined and abnormality categories that cannot be appropriately determined at times. Therefore, for example, this information can also be included in the abnormality category information pre-stored in the storage unit 140.

[0121] Therefore, for example, if the anomaly classification unit 132 determines that the measurement is in a confined space (c), the user can confirm this by displaying a message on the display unit 150 stating "In the absence of a workpiece, it is sometimes impossible to make a proper determination" or a similar notification. Furthermore, in order to properly determine the anomaly category desired by the user, the anomaly category to be determined or the conditions for proper determination according to each anomaly category can be displayed on the display unit 150 in advance for the user's confirmation.

[0122] Furthermore, the user can input a time period that meets appropriate conditions (e.g., the presence of a workpiece and a base in this embodiment) via the operation unit 160. Thus, it can be determined that the anomaly category determined during that time period is the result of an appropriate determination.

[0123] Furthermore, in this embodiment, as the exception category information stored in the storage unit 140, examples include: Figure 5 The table shown is an example of a table that maps the feature information used for anomaly category determination to the monitoring method for each anomaly category, but it is not limited to this. For example, it could also be a table that maps feature information to anomaly category for each monitoring method.

[0124] Figure 12 This is a diagram illustrating an example of anomaly category information obtained by mapping the feature information used to the predicted anomaly category for each monitoring method. For example... Figure 12 As shown, in the monitoring methods (A) to (C), if any feature information from (1) to (6) is monitored, it is possible to determine whether the anomaly category of (a) to (f) can be determined. Here, there are cases where the anomaly category can be uniquely determined by the combination of the monitoring method and the feature information, and cases where the anomaly category can be determined by multiple combinations of these features.

[0125] Furthermore, when using a reference value difference as a monitoring method, a reference value needs to be preset. However, when using short-term sudden changes and light waveforms at a certain time point, it is possible to determine the anomaly by comparing and changing the data stored in the storage unit 140. Therefore, sometimes it is possible to determine the anomaly category even if a reference value is not preset.

[0126] Thus, the form in which the anomaly category information stored in the storage unit 140 is adopted depends on the type of displacement sensor 10, the anomaly category desired by the user, and the specific determination and processing steps in the anomaly category determination unit 132. Furthermore, multiple forms of anomaly category information can be stored in the storage unit 140, or... Figure 5 and Figure 12 Exception category information is stored in a form other than the exception category information shown.

[0127] The embodiments described above are merely for ease of understanding of the present invention and are not intended to limit or interpret the invention. The elements, configurations, materials, conditions, shapes, and dimensions of the embodiments are not limited to the examples and can be appropriately modified. Furthermore, the structures shown in different embodiments can be partially substituted for or combined with each other.

[0128] [Postscript]

[0129] A displacement sensor (10) having:

[0130] The light-projecting unit (110) projects light onto the inspection area;

[0131] The light-receiving unit (120) receives light reflected from the inspection area and outputs a light-receiving waveform;

[0132] The storage unit (140) pre-stores anomaly category information, which is obtained by mapping multiple feature information contained in the light-received waveform to multiple anomaly categories predicted based on the multiple feature information;

[0133] The feature information extraction unit (131) extracts at least two types of feature information based on the light-receiving waveform output by the light-receiving unit;

[0134] An anomaly category determination unit (132) determines, based on the extracted at least two or more feature information, at least one anomaly category from a plurality of anomaly categories pre-stored in the storage unit; and

[0135] The output unit (133) outputs the determined anomaly category.

[0136] Label Explanation

[0137] 10: Displacement sensor; 11: Cable; 100: Sensor head; 110: Light projection unit; 111: Light-emitting element; 112: Light projection control circuit; 120: Light receiving unit; 121: Camera element; 122: Signal processing circuit; 123: A / D conversion circuit; 130: Control unit; 131: Feature information extraction unit; 132: Anomaly category determination unit; 133: Output unit; 140: Storage unit; 150: Display unit; 160: Operation unit; 170: Input / output interface; L1: Laser; L2: Reflected light; B: Base; W: Workpiece; M10: Status monitoring method; S11~S19: Steps of status monitoring method M10; M100: Anomaly category determination method; S101~S116: Steps of anomaly category determination method M100.

Claims

1. A displacement sensor, wherein, This displacement sensor has the following characteristics: The projection unit projects light onto the inspection area; The light-receiving part receives light reflected from the inspection area and outputs the light-receiving waveform; The storage unit pre-stores anomaly category information, which is obtained by mapping multiple feature information contained in the light-received waveform to multiple anomaly categories predicted based on the multiple feature information; The feature information extraction unit extracts at least two types of feature information based on the light-receiving waveform output by the light-receiving unit; An anomaly category determination unit, based on the extracted at least two types of feature information, determines at least one anomaly category from a plurality of anomaly categories pre-stored in the storage unit; and The output unit outputs the determined exception category.

2. The displacement sensor according to claim 1, wherein, The anomaly category determination unit uses at least one monitoring method selected from the information contained in the difference component with respect to the reference value, the amount of change within a specified period, and the light waveform at a certain point in time to determine the anomaly category based on the extracted feature information.

3. The displacement sensor according to claim 2, wherein, Among the multiple anomaly categories pre-stored in the storage unit, the anomaly categories that can be determined according to the monitoring method are classified.

4. The displacement sensor according to any one of claims 1 to 3, wherein, The feature information includes at least two of the following: light intensity, light intensity adjustment parameters, width of the light-receiving waveform, number of surfaces of the light-receiving waveform, total area of ​​the light-receiving waveform, and background level.

5. The displacement sensor according to claim 4, wherein, The anomaly category determination unit determines the anomaly category based on a combination of at least two of the following: the amount of light received, the light received adjustment parameter, the width value of the light received waveform, the number of surfaces of the light received waveform, the total area of ​​the light received waveform, and the background level.

6. The displacement sensor according to claim 5, wherein, Among the multiple anomaly categories pre-stored in the storage unit, anomaly categories that can be determined based on at least two combinations of the light received amount, the light received amount adjustment parameter, the width value of the light received waveform, the number of surfaces of the light received waveform, the total area of ​​the light received waveform, and the background level are classified.

7. The displacement sensor according to any one of claims 1 to 3, wherein, Among the multiple anomaly categories pre-stored in the storage unit, anomaly categories that can be determined based on the presence or absence of workpieces and bases in the inspection area are classified.

8. The displacement sensor according to any one of claims 1 to 3, wherein, The output unit outputs a countermeasure or presumed cause corresponding to the determined anomaly category.

9. A state monitoring method, performed by a displacement sensor including a processor, wherein, This status monitoring method includes: The lighting step involves projecting light onto the inspection area; The light receiving step involves receiving light reflected from the inspection area and outputting the light-receiving waveform. The feature information extraction step extracts at least two types of feature information based on the light-receiving waveform output in the light-receiving step. The anomaly category determination step, based on the extracted at least two types of feature information, determines the anomaly category of at least one of multiple anomaly categories pre-stored in memory, which are obtained by mapping multiple feature information contained in the light-received waveform to multiple anomaly categories predicted based on the multiple feature information; and The output step outputs the determined anomaly category.

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