JP7662997B2 - Displacement sensor and condition monitoring method - Google Patents

Description

The present invention relates to a displacement sensor and a condition monitoring method.

Conventionally, optical displacement sensors have been used as devices for measuring the displacement of a workpiece without contact. However, such displacement sensors may not be able to properly measure the workpiece due to deterioration of the displacement sensor over time, contamination caused by the adhesion of dust and other foreign matter, and influences from the surrounding environment.

Patent Document 1 discloses technology relating to a detection device that detects signs of an abnormality in a detection sensor before an abnormality occurs in the detection sensor. Specifically, the detection device disclosed in Patent Document 1 acquires the amount of light received by the light receiving unit at regular intervals when a workpiece passes through a detection position, and generates a received light waveform based on the acquired results. The detection device then compares the reference waveform with the received light waveform to determine whether or not there are signs of an abnormality in the detection sensor.

JP 2012-78175 A

However, the detection device disclosed in Patent Document 1 determines whether there are signs of an abnormality in the detection sensor, but even if it detects signs of an abnormality, it is unable to identify the cause and details. This causes a problem for the user in that they are unable to grasp what specific measures they need to take afterwards.

The present invention aims to provide a displacement sensor and a condition monitoring method that can appropriately determine the type of abnormality.

A displacement sensor according to one aspect of the present invention includes 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 received light waveform, a storage unit that stores in advance abnormality type information that associates multiple pieces of feature information included in the received light waveform with multiple abnormality types predicted from the multiple pieces of feature information, a feature information extraction unit that extracts at least two or more types of feature information based on the received light waveform output by the light-receiving unit, an abnormality type determination unit that determines at least one or more abnormality types from the multiple abnormality types in the abnormality type information pre-stored in the storage unit based on the at least two or more types of extracted feature information, and an output unit that outputs the determined abnormality type. Here, the received light waveform is generated as a waveform that indicates the amount of light received for each pixel for the light received by the image sensor of the light-receiving unit.

According to this aspect, the feature information extraction unit extracts at least two pieces of feature information based on the received light waveform, and the abnormality type determination unit can appropriately determine at least one type of abnormality among the multiple abnormality types in the abnormality type information pre-stored in the storage unit based on the feature information.

In the above aspect, the anomaly type determination unit may determine the type of anomaly for the extracted characteristic information by using at least one of the following monitoring methods: the difference between the characteristic information and a reference value, the amount of change over a specified period of time, and information contained in the received light waveform at a certain point in time.

According to this aspect, the abnormality type determination unit determines the abnormality type using multiple types of monitoring methods, so it can more appropriately determine the abnormality type.

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

According to this aspect, the abnormality type information classifies the abnormality types that can be determined according to the monitoring method, so that an appropriate monitoring method can be used to determine the abnormality type according to each abnormality type.

In the above aspect, the characteristic information may include at least two of the amount of received light, a light-receiving amount adjustment parameter, a width value of the received light waveform, the number of faces of the received light waveform, the total area of the received light waveform, and a background level.

According to this aspect, the characteristic information extracted from the received light waveform includes at least two of the received light amount, the received light amount adjustment parameter, the width value of the received light waveform, the number of faces of the received light waveform, the total area of the received light waveform, and the background level, so that the type of abnormality can be determined more specifically based on these.

In the above aspect, the abnormality type determination unit may determine the abnormality type based on a combination of at least two of the amount of received light, the light-receiving amount adjustment parameter, the width value of the received light waveform, the number of faces of the received light waveform, the total area of the received light waveform, and the background level.

According to this aspect, the abnormality type determination unit determines each abnormality type by appropriately combining multiple pieces of characteristic information, so that the abnormality type can be determined more appropriately.

In the above aspect, among the multiple abnormality types in the abnormality type information prestored in the storage unit, the abnormality types that can be determined may be classified according to a combination of at least two or more of the amount of received light, the amount of received light adjustment parameter, the width value of the received light waveform, the number of faces of the received light waveform, the total area of the received light waveform, and the background level.

According to this aspect, the abnormality type information classifies the abnormality types that can be determined according to a combination of at least two of the amount of received light, the light-receiving-amount adjustment parameter, the width value of the received light waveform, the number of faces of the received light waveform, the total area of the received light waveform, and the background level, so that when determining the abnormality type, it is possible to appropriately combine characteristic information according to each abnormality type.

In the above aspect, among the multiple abnormality types in the abnormality type information pre-stored in the storage unit, an abnormality type that can be determined depending on the presence or absence of a workpiece and a base in the inspection area may be classified. Note that the workpiece is a measurement object that is measured by the displacement sensor, and the base is a base on which the workpiece is placed.

According to this aspect, the type of abnormality can be appropriately determined depending on the presence or absence of a workpiece and a base in the inspection area.

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

This allows the user to directly and specifically understand what measures they should take.

A condition monitoring method according to one aspect of the present invention is a condition monitoring method executed by a displacement sensor including a processor, and includes a light projection step of projecting light onto an inspection area, a light reception step of receiving light reflected from the inspection area and outputting a received light waveform, a feature information extraction step of extracting at least two or more types of feature information based on the received light waveform output in the light reception step, an abnormality type determination step of determining at least one or more abnormality types from among multiple abnormality types in abnormality type information that is stored in advance in a memory and that associates multiple pieces of feature information included in the received light waveform with multiple abnormality types predicted from the multiple pieces of feature information, based on the at least two or more types of extracted feature information, and an output step of outputting the determined abnormality type.

According to this aspect, at least two pieces of feature information are extracted based on the received light waveform in the feature information extraction step, and at least one type of abnormality can be appropriately determined from among the multiple abnormality types in the abnormality type information pre-stored in the memory based on the feature information in the abnormality type determination step.

The present invention provides a displacement sensor and a condition monitoring method that can appropriately determine the type of abnormality.

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. 1 is a block diagram showing the functions constituting a displacement sensor 10 according to an embodiment of the present invention. 2 is a block diagram showing a control unit 130 and each of the functional configurations related thereto, which are involved in a process of determining an abnormality type, in the displacement sensor 10 according to an embodiment of the present invention. FIG. 11A and 11B are diagrams illustrating an example of feature information extracted from a received light waveform. 11 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. FIG. 13A is a diagram showing an example of determining sensor contamination. FIG. 1B is a diagram showing an example of determining deterioration of a light source. FIG. 13C is a diagram showing an example of determining narrow space measurement. FIG. 1D is a diagram showing an example of determining multiple reflections. FIG. 10E is a diagram showing an example of determining mutual interference. FIG. 10F is a diagram showing an example of determining disturbance light. FIG. 11 is a diagram showing an example in which an abnormality type is displayed on a display unit 150 provided in the amplifier unit. FIG. 11 is a diagram illustrating an example of error codes and countermeasures associated with abnormality types. 10 is a flowchart showing the process flow of a condition monitoring method M10 for monitoring the condition of the displacement sensor 10 and its surrounding environment, including determining the type of abnormality, while the displacement sensor 10 measures a workpiece W. 10 is a flowchart showing a specific process flow of the abnormality type determination method M100 executed in steps S15 to S18 in FIG. 9. 6 is a diagram showing an example of an abnormality type that cannot be determined depending on the presence or absence of a workpiece and a base in an inspection area, among the abnormality types in FIG. 5 . 11 is a diagram showing an example of abnormality type information in which characteristic information used and predicted abnormality types are associated with each other for each monitoring method. FIG.

The following describes an embodiment of the present invention in detail with reference to the drawings. Note that the embodiment described below is merely a specific example for implementing the present invention, and is not intended to limit the interpretation of the present invention. In addition, to facilitate understanding of the description, the same components in each drawing are given the same reference numerals as much as possible, and duplicate descriptions may be omitted.

<One embodiment>
[Configuration of Displacement Sensor]
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 that is connected via a cable 11.

As shown in FIG. 1, the displacement sensor 10 projects laser light L1 from the sensor head 100 onto the workpiece W placed on the base B, and receives reflected light L2 from the workpiece W in response to the laser light L1, thereby measuring the amount of displacement of the surface of the workpiece W based on the principle of triangulation. Note that the amount of displacement measured is the distance from the sensor head 100 to the surface of the workpiece W, and the distance can be output as detection data.

As described above, the displacement sensor 10 is typically a measuring device that projects laser light onto the workpiece W to measure the amount of displacement on the surface of the workpiece W. However, in this embodiment, for example, when the workpiece W is not present, and the inspection area is equipment such as an assembly robot or the surrounding environment, the displacement sensor 10 may project laser light and receive reflected light from the inspection area to monitor the condition.

Figure 2 is a block diagram showing the functions constituting the displacement sensor 10 according to one embodiment of the present invention. As shown in Figure 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. The light-projecting unit 110 includes a light-emitting element 111 and a light-projection control circuit 112, and the light-receiving unit 120 includes an image sensor 121, a signal processing circuit 122, and an A/D conversion circuit 123.

For example, the light projecting unit 110, the light receiving unit 120, the control unit 130, and the memory unit 140 are incorporated in the sensor head 100 shown in FIG. 1, and the display unit 150, the operation unit 160, and the input/output interface 170 are provided in the amplifier unit. However, it is not necessarily necessary to separate the sensor head 100 from the amplifier unit, and all of the components shown in FIG. 2 may be provided in a single housing.

The light-projecting unit 110 projects light onto an inspection area (e.g., a workpiece W). Specifically, the light-projecting unit 110 has a laser diode (LD) as a light-emitting element 111, and the light-emitting element 111 is driven to project light while the light-emitting intensity and light-emitting time of the light-emitting element 111 are adjusted by the light-projection control circuit 112. The light-projection control circuit 112 operates based on commands from the control unit 130.

The light receiving unit 120 receives light reflected from the inspection area and outputs a received light waveform. Specifically, the light receiving unit 120 has a CMOS including multiple pixels as the image sensor 121, and further has a signal processing circuit 122 and an A/D conversion circuit 123 for processing the image signal generated by the image sensor 121. The signal processing circuit 122 controls the timing of the operation of the image sensor 121 based on instructions from the control unit 130, and also captures the image generated by the image sensor 121 and outputs it to the A/D conversion circuit 123. The image that has been analog-to-digital converted by the A/D conversion circuit 123 is then input to the control unit 130 for measurement processing.

The control unit 130 is, for example, a CPU, and based on the program and setting data stored in the memory unit 140, causes the light projection unit 110 to emit laser light L1 and operates the light receiving unit 120 in accordance with the timing of the emission to receive reflected light L2 from the workpiece W. The control unit 130 then measures the amount of displacement of the workpiece W through various processes based on the received light waveform output by the light receiving unit 120.

The control unit 130 also extracts feature information contained in the received light waveform output by the light receiving unit 120, and determines the type of abnormality inferred from the extracted feature information. 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 EEPROM, and stores programs, setting data for defining various operations controlled by the control unit 130, and abnormality type information used in the process of determining the abnormality type described below. It is also configured as a buffer function for storing data such as the received light waveform output by the light receiving unit 120 and feature information extracted from the received light waveform, which are used in the process of determining the abnormality type described below.

The display unit 150 is configured, for example, with a liquid crystal display or an organic EL display, and displays information to be notified to the user, such as the measurement values from the control unit 130 described above, the abnormality types described below, measures to be taken to address the abnormality types, and other settings and states related to the displacement sensor 10.

The operation unit 160 is composed of buttons, dials, a touch panel, or the like, and is used to allow the user to, for example, turn the power of the displacement sensor 10 on and off, and switch between various settings and operating modes.

The input/output interface 170 is connected to external devices such as a PC (personal computer) and a PLC (programmable logic controller). When connected to an external device, it becomes possible to perform operations on the external device similar to those performed on the operation unit 160. For example, operations on the displacement sensor 10 may be performed on the PC, and the measurement results obtained by the displacement sensor 10 may be displayed on the screen of the PC.

[Processing for Determining Abnormality Type]
Next, a detailed description will be given of the abnormality type determination process performed by the displacement sensor 10. Here, the abnormality type determination process is a function of detecting an abnormality or a symptom thereof that makes it impossible to properly measure the workpiece W or the like due to, for example, deterioration of the displacement sensor 10 over time, contamination due to the adhesion of foreign matter such as dust, and influences from the surrounding environment.

Figure 3 is a block diagram showing the control unit 130 and each of its associated functional configurations involved in the abnormality type determination process in the displacement sensor 10 according to one embodiment of the present invention. As shown in Figure 3, the control unit 130 includes a feature information extraction unit 131, an abnormality type determination unit 132, and an output unit 133, and determines the abnormality type while referring to the memory unit 140 and outputs the determined abnormality type to the display unit 150.

The feature information extraction unit 131 extracts at least two types of feature information based on the received light waveform output by the light receiving unit 120. Specifically, the feature information extraction unit 131 extracts multiple pieces of feature information (described later) based on the received light waveform continuously output by the light receiving unit 120, and stores the information in the storage unit 140.

Figure 4 shows an example of feature information extracted from the received light waveform. In the figure, the pixel position indicates the position in the baseline direction in triangulation with respect to the pixel on the image sensor 121, and the LSB indicates the average amount of received light of the pixel corresponding to the pixel position. As shown in Figure 4, (1) the amount of received light, (2) the light-receiving amount adjustment parameter, (3) width value, (4) number of faces, (5) total area of the received light waveform, and (6) background level are extracted from the received light waveform.

Note that (1) the amount of received light is the amount of light received by the light receiving unit 120, and (2) the light receiving amount adjustment parameter is the adjustment (e.g., gain, exposure time, etc.) required to output the received light waveform according to the amount of light received by the light receiving unit 120. (3) The width value is, for example, the width (half-width) of the received light waveform at 50% of the peak amount of received light, and indicates the spread of the received light waveform, and (4) the number of faces is the number of peaks of the received light waveform, and indicates the number of light components. (5) The total area of the received light waveform is, for example, the total area occupied by the received light waveform shown in FIG. 4, and (6) the background level indicates the amount of received light due to ambient light received by the light receiving unit 120 without the displacement sensor 10 projecting measurement light. The width value is not limited to the width (half-width) of the received light waveform at 50% of the peak received light amount, but may be, for example, the width value of the received light waveform at 40% or 60% of the peak received light amount, as long as it indicates the spread of the received light waveform.

In this manner, in this embodiment, the feature information extraction unit 131 extracts the six pieces of feature information (1) to (6) described above from the received light waveform output by the light receiving unit 120.

The abnormality type determination unit 132 determines at least one of the multiple abnormality types in the abnormality type information pre-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 above-mentioned feature information (1) to (6) by time series changes and combinations of these. Here, the storage unit 140 pre-stores abnormality type information in which multiple pieces of feature information included in the received light waveform are associated with multiple abnormality types predicted from the multiple pieces of feature information. The abnormality type determination unit 132 determines the abnormality type based on the above-mentioned feature information (1) to (6) while referring to the abnormality type information stored in the storage unit 140.

Figure 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, six abnormality types, (a) sensor dirt, (b) light source degradation, (c) narrow space measurement, (d) multiple reflections, (e) mutual interference, and (f) ambient light, are determined according to the above-mentioned (1) amount of received light, (2) light-receiving amount adjustment parameter, (3) width value, (4) number of faces, (5) total area of received light waveform, and (6) background level value, time series changes, and combinations of these.

Furthermore, when the abnormality type determination unit 132 determines the above-mentioned abnormality types (a) to (f) based on the above-mentioned characteristic information (1) to (6), the monitoring method used for the determination is associated. The monitoring method includes, for example, a method of monitoring (A) the difference amount from a reference value, (B) the amount of change in a specified period, and (C) information contained in the received light waveform at a certain point in time for the above-mentioned characteristic information (1) to (6), and at least one of these monitoring methods is used.

(A) The difference from the reference value is compared with 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 start of work) as a reference value (threshold value), (B) the amount of change in a specified period is monitored by monitoring the amount of change in a short period of, for example, several ms to several hundred ms, and (C) the information contained in the received light waveform at a certain point in time is detected by detecting information contained in a temporary waveform of the received light waveform.

Below, we will explain the abnormality types (a) to (f) in detail, providing specific examples.

Figure 6A is a diagram showing an example of determining sensor contamination (a). As shown in Figure 6A, when there is sensor contamination, the amount of received light, the light reception amount adjustment parameter, and the width value change (sudden change in a short time). Furthermore, when the range of reference values (threshold values) that allow the displacement sensor 10 to perform appropriate measurements on the workpiece is exceeded, the anomaly type determination unit 132 determines that there is sensor contamination. Note that if the received light waveform contains noise components, the total area of the received light waveform and the background level may also change, and this characteristic information may also be used to determine sensor contamination.

Figure 6B is a diagram showing an example of determining (b) light source degradation. As shown in Figure 6B, when there is light source degradation, the displacement sensor 10 is in a state where the amount of light received decreases and the gain, which is a light reception adjustment parameter, is increased. For example, when the initial reference value of the displacement sensor 10 (a value detected by teaching when first used) is compared with the range of reference values (threshold values) that allow appropriate measurement of the workpiece, the abnormality type determination unit 132 determines that the light source has deteriorated.

Figure 6C is a diagram showing an example of determining (c) narrow space measurement. As shown in Figure 6C, when narrow space measurement is performed, the displacement sensor 10 may experience an unstable state of the amount of received light, causing the received light waveform to fluctuate up and down, or the amount of received light may decrease, causing the gain, which is the light reception amount adjustment parameter, to be increased. Here, the difference from the above-mentioned (b) light source deterioration is that in the case of narrow space measurement, for example, when the state of the inspection area (workpiece) changes (when the narrow space position is moved out), the amount of received light and the light reception amount adjustment parameter return to their original state. In other words, when narrow space measurement is performed, if the change in the amount of received light and the light reception amount adjustment parameter is temporary, the anomaly type determination unit 132 determines that narrow space measurement is performed.

Figure 6D is a diagram showing an example of determining (d) multiple reflections. As shown in Figure 6D, when there are multiple reflections, a change occurs in which the number of faces increases (a short-term sudden change). Then, when the measurement value that allows the displacement sensor 10 to perform appropriate measurement of the workpiece and the parameters extracted from the received light waveform (width value, number of faces, etc.) exceed the range of reference values (threshold values), the anomaly type determination unit 132 determines that there are multiple reflections. Furthermore, there are cases where the amount of received light decreases (the light reception amount adjustment parameter increases), and this characteristic information may also be used to determine sensor contamination.

Figure 6E is a diagram showing an example of determining (e) mutual interference. As shown in Figure 6E, when there is mutual interference, the number of faces increases, and if the interference light flashes periodically, for example, the peak waveform does not change and the number of faces changes repeatedly (sudden change in a short time). In such a case, the anomaly type determination unit 132 determines that there is mutual interference. Furthermore, the amount of received light and the light receiving amount adjustment parameters may change, and the background level may also change, and this characteristic information may also be used to determine mutual interference.

Figure 6F is a diagram showing an example of determining (f) disturbance light. As shown in Figure 6F, when disturbance light is present, changes occur in the amount of received light, the light-receiving-amount adjustment parameter, the width value, the total area of the received light waveform, and the background level (short-term sudden change). In particular, the background level increases but may also decrease, which is different from the above-mentioned (a) sensor contamination in this respect. Then, when the range of reference values (threshold values) that allow the displacement sensor 10 to perform appropriate measurements on the workpiece is exceeded, the anomaly type determination unit 132 determines that disturbance light has occurred.

The conditions for determining the type of abnormality are not limited to those described here. For example, if the type of abnormality can be determined using conditions other than those described here, other conditions may be used for the determination. Also, if the type of abnormality can be determined using combinations of feature information other than those described here, other combinations may be used for the determination.

Here, the abnormality type determination unit 132 determines the abnormality types (a) to (f) using the monitoring methods (A) to (C) based on the feature information (1) to (6), but is not limited to these. For example, the abnormality types may include seepage, head tilt, transparent body detection, etc., and the feature information may include information on the tilt or center of gravity of the received light waveform, or other feature information that can be extracted from the received light waveform. Furthermore, the monitoring method may be, for example, a continuous monitoring method, or an intermittent or periodic (long-term and short-term) monitoring method. In addition, it is not necessary to apply all of the above-mentioned abnormality types, feature information, and monitoring methods, and they may be appropriately selected so that the abnormality type can be appropriately determined according to the abnormality type, accuracy, performance, etc. desired by the user.

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 type so as to display it on the display unit 150, or may output the abnormality type so as to notify a PC or PLC connected as an external device.

Figure 7 is a diagram showing an example of an abnormality type displayed on the display unit 150 provided in the amplifier unit. As shown in Figure 7, the abnormality type "sensor dirt" is displayed. This allows the user to recognize the sensor dirt and take action.

The content displayed on the display unit 150 is not limited to the type of abnormality, but may instead display measures or presumed causes corresponding to the type of abnormality, such as "Check if the sensor is dirty" or "Replace the light source." This allows the user to directly and specifically understand what measures should be taken.

Figure 8 is a diagram showing an example of error codes and countermeasures associated with an abnormality type. For example, a correspondence table such as that shown in Figure 8 may be stored in advance in the storage unit 140, and the output unit 133 may refer to the correspondence table and output one or more of the abnormality type, error code, and countermeasure according to the abnormality type determined by the abnormality type determination unit 132.

In addition, if the display screen of the display unit 150 is small, for example, an error code associated with the type of abnormality can be displayed, and the user can then understand what measures to take in response to the displayed error code by, for example, referring to a table prepared in advance that associates error codes with types of abnormalities and countermeasures, etc.

Furthermore, the contents output by the output unit 133 and the contents displayed by the display unit 150 may be switched depending on the situation, such as when the displacement sensor 10 is inspected periodically and when the workpiece W is actually being measured. For example, when an abnormality type that affects the measurement result is determined during operation when the workpiece W is being measured, the output unit 133 may output a warning signal and replace the measurement result with an indefinite value or an error, and may further display this on the display unit 150. Furthermore, when detailed information on the abnormality type is required, continuous time series data and waveforms, etc. may be displayed based on the received light waveform or characteristic information data stored in the memory unit 140.

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

Figure 9 is a flowchart showing the process flow of a condition monitoring method M10 in which the displacement sensor 10 measures the workpiece W while monitoring the condition of the displacement sensor 10 and its surrounding environment, including determining the type of abnormality. As shown in Figure 9, the condition 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 with reference to FIG. 1, the displacement sensor 10 projects laser light L1 onto the workpiece W and receives reflected light L2 from the workpiece W in response to the laser light L1, thereby measuring the amount of displacement of the surface of the workpiece W.

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

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 received light waveform output in step S12. Specific examples of the feature information extracted include (1) the amount of received light, (2) the light-receiving-amount adjustment parameter, (3) the width value, (4) the number of faces, (5) the total area of the received light waveform, and (6) the background level, which are described with reference to FIG. 4.

In step S14, the control unit 130 stores the feature information extracted in step S13 in the memory unit 140.

In steps S15 to S18, the abnormality type determination unit 132 in the control unit 130 determines the abnormality type based on the characteristic information accumulated in step S14 while referring to the abnormality type information previously stored in memory. As a specific example, as described with reference to FIG. 5, the abnormality type determination unit 132 determines six abnormality types, namely (a) sensor dirt, (b) light source deterioration, (c) narrow space measurement, (d) multiple reflections, (e) mutual interference, and (f) ambient light, for six pieces of characteristic information, namely (1) amount of received light, (2) light-receiving-amount adjustment parameter, (3) width value, (4) number of faces, (5) total area of received light waveform, and (6) background level, using three monitoring methods, namely (A) reference value difference, (B) short-term sudden change, and (C) received light waveform at a certain point in time.

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

The series of processes related to determining the type of abnormality in steps S13 to S19 may be performed every measurement cycle, or may be performed as needed.

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

Here, the anomaly type determination unit 132 determines the anomaly type by monitoring the change (increase or decrease) of each characteristic information item in a short time, and whether or not the change is within the range of a reference value (above or below). The processing in each of these steps will be described in detail below.

In step S101, if (1) the amount of received light falls below the initial reference value and (2) the light-receiving-amount adjustment parameter exceeds the initial reference value (Yes in step S101), the anomaly type determination unit 132 determines that (b) the light source has deteriorated (step S102).

The initial reference value here is set, for example, based on a value detected by teaching when the displacement sensor 10 is purchased and used for the first time. In other words, it is used to grasp the amount of change (difference) in (1) the amount of light received and (2) the light-receiving amount adjustment parameter from when the displacement sensor 10 is used for the first time. The initial reference value is preferably set within a range for the amount of light received and the light-receiving amount adjustment parameter that allows the displacement sensor 10 to properly measure the workpiece.

If teaching is not performed and an initial reference value is not set when the displacement sensor 10 is purchased and used for the first time, 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 may determine (b) light source degradation by using a value preset in the displacement sensor 10 or by the user setting it.

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

The reference value is set based on a value detected by teaching, for example, at the start of work when the displacement sensor 10 is operated. In other words, it is used to grasp the amount of change (difference) in the characteristic information from the start of work when the displacement sensor 10 is operated. The reference value is set within a range in which the displacement sensor 10 can appropriately measure the workpiece with respect to the characteristic information (including the characteristic information described below). The reference value may be updated for each teaching, or if teaching is not performed at the start of work when the displacement sensor 10 is operated, a value previously set in the displacement sensor 10 may be used or may be set by the user.

In step S104 (Yes in step S103), if (4) the number of faces does not decrease and is not within the reference value range (No in step S104), the anomaly type determination unit 132 determines that (d) there is multiple reflection (step S105).

In step S106 (Yes in step S104), if (4) the number of faces repeatedly increases and decreases (Yes in step S106), the anomaly type determination unit 132 determines that (e) mutual interference has occurred (step S107).

In step S108 (No in step S103), the anomaly type determination unit 132 determines whether (1) the amount of received light has decreased and is below the reference value, or (2) the light-receiving amount adjustment parameter has increased and is above the reference value.

In step S109 (Yes in step S108), if (3) the width value has increased (Yes in step S109), the abnormality type determination unit 132 determines that (a) the sensor is dirty (step S110).

In step S111 (No in step S109), if (1) the amount of received light is increasing and (2) the light-receiving amount adjustment parameter is decreasing (Yes in step S111), the abnormality type determination unit 132 determines that (c) a narrow space measurement has been performed (step S112).

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

In step S114 (Yes in step S113), the anomaly 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 received light waveform has decreased and is below the reference value, and (6) the background level has decreased and is below the reference value (Yes in step S115), the anomaly type determination unit 132 determines that there is disturbance light (step S116).

In this way, the anomaly type determination unit 132 determines six types of anomalies: (a) sensor contamination, (b) light source degradation, (c) narrow space measurement, (d) multiple reflections, (e) mutual interference, and (f) ambient light.

As described above, according to the displacement sensor 10 and the condition monitoring method M10 of one embodiment of the present invention, the feature information extraction unit 131 extracts multiple pieces of feature information from the received light waveform, and the abnormality type determination unit 132 can determine at least one abnormality type from the multiple abnormality types in the abnormality type information pre-stored in the storage unit 140 based on the multiple pieces of feature information. As a result, the abnormality type can be appropriately determined, and the user can take measures to address the abnormality type.

In this embodiment, the types of abnormality (a) to (f) shown in FIG. 5 are judged. However, the types of abnormality that can be judged may differ depending on the state of the inspection area.

Figure 11 is a diagram showing an example of anomaly types in Figure 5 that cannot be judged depending on the presence or absence of a workpiece and base in the inspection area. As shown in Figure 11, for anomaly types (a) to (f), if a workpiece is present in the inspection area, it is possible to make an appropriate judgment. However, if a workpiece and base are not present in the inspection area, it may not be possible to make an appropriate judgment for (a) sensor contamination, (c) narrow space measurement, and (d) multiple reflections.

As such, depending on the conditions of the inspection area, there are abnormality types that can be appropriately determined and abnormality types that may not be appropriately determined. For example, this information may be included in the abnormality type information stored in advance in the storage unit 140.

As a result, for example, when the abnormality type determination unit 132 determines that (c) narrow space measurement has occurred, the display unit 150 may display a message stating "If no workpiece is present, an appropriate determination may not be made" or a similar notification may be given to have the user confirm this. In addition, the abnormality type to be determined or the conditions for appropriate determination for each abnormality type may be displayed in advance on the display unit 150 to allow the user to properly determine the abnormality type desired.

Furthermore, the user may input a time period that satisfies appropriate conditions (for example, in this embodiment, the presence of a workpiece and a base) via the operation unit 160. This allows the user to know that the type of abnormality determined during that time period is an appropriate result.

In addition, in this embodiment, as an example of the abnormality type information stored in the storage unit 140, a table in which characteristic information and a monitoring method used to determine the abnormality type are associated with each abnormality type as shown in FIG. 5 has been given, but this is not limited to this. For example, a table in which characteristic information and an abnormality type are associated with each monitoring method may also be used.

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

When the reference value difference is used as the monitoring method, the reference value must be set in advance. However, when a short-term sudden change and a received light waveform at a certain point in time are used, the type of abnormality can be determined by comparing with the data stored in the memory unit 140 and by changes, etc., so that it may be possible to determine the type of abnormality even if a reference value is not set in advance.

As described above, the format of the abnormality type information to be stored in the memory unit 140 may be determined according to the type of the displacement sensor 10, the abnormality type desired by the user, and the specific determination process procedure in the abnormality type determination unit 132. In addition, the memory unit 140 may store abnormality type information in multiple formats, or may store abnormality type information in a format other than the abnormality type information shown in FIG. 5 and FIG. 12.

The above-described embodiments are intended to facilitate understanding of the present invention, and are not intended to limit the present invention. The elements of the embodiments, as well as their arrangements, materials, conditions, shapes, sizes, etc., are not limited to those exemplified, and may be modified as appropriate. Furthermore, configurations shown in different embodiments may be partially substituted or combined.

[Additional Note]
A light projection unit (110) that projects light onto an inspection area;
a light receiving unit (120) that receives light reflected from the inspection area and outputs a received light waveform;
a storage unit (140) in which abnormality type information in which a plurality of pieces of characteristic information included in the received light waveform are associated with a plurality of abnormality types predicted from the plurality of pieces of characteristic information is stored in advance;
a feature information extraction unit (131) for extracting at least two types of feature information based on a received light waveform output by the light receiving unit;
an abnormality type determination unit (132) that determines at least one abnormality type among a plurality of abnormality types in the abnormality type information pre-stored in the storage unit based on the at least two or more types of extracted feature information;
an output unit (133) for outputting the determined abnormality type;
A displacement sensor (10).

10...displacement sensor, 11...cable, 100...sensor head, 110...light projecting unit, 111...light emitting element, 112...light projecting control circuit, 120...light receiving unit, 121...image sensor, 122...signal processing circuit, 123...A/D conversion circuit, 130...control unit, 131...characteristic information extraction unit, 132...abnormality type determination unit, 133...output unit, 140...storage unit, 150...display unit, 160...operation unit, 170...input/output interface, L1...laser light, L2...reflected light, B...base, W...work, M10...condition monitoring method, S11-S19...each step of condition monitoring method M10, M100...abnormality type determination method, S101-S116...each step of abnormality type determination method M100

Claims (9)

a light projection unit that projects light onto an inspection area;
a light receiving section that receives light reflected from the inspection area and outputs a received light waveform;
a storage unit that stores in advance abnormality type information in which a plurality of pieces of characteristic information included in the received light waveform are associated with a plurality of abnormality types predicted from the plurality of pieces of characteristic information;
a feature information extracting unit that extracts at least two types of feature information based on a received light waveform output by the light receiving unit;
an abnormality type determination unit that determines at least one abnormality type among a plurality of abnormality types in the abnormality type information pre-stored in the storage unit based on the at least two or more types of extracted feature information;
an output unit that outputs the determined abnormality type;
A displacement sensor comprising: the abnormality type determination unit determines an abnormality type for the extracted characteristic information by using at least one of a difference amount from a reference value, an amount of change in a predetermined period, and information included in a received light waveform at a certain point in time;
The displacement sensor according to claim 1 . Among a plurality of abnormality types in the abnormality type information pre-stored in the storage unit, an abnormality type that can be determined according to the monitoring method is classified.
The displacement sensor according to claim 2 . The characteristic information includes at least two of the amount of received light, a light-receiving amount adjustment parameter, a width value of the received light waveform, a number of faces of the received light waveform, a total area of the received light waveform, and a background level.
The displacement sensor according to claim 1 . the abnormality type determination unit determines an abnormality type according to a combination of at least two or more of the amount of received light, the light-receiving amount adjustment parameter, a width value of the received light waveform, a number of faces of the received light waveform, a total area of the received light waveform, and the background level;
The displacement sensor according to claim 4. Among the multiple abnormality types in the abnormality type information pre-stored in the storage unit, the abnormality types that can be determined are classified according to a combination of at least two or more of the amount of received light, the light receiving amount adjustment parameter, the width value of the received light waveform, the number of faces of the received light waveform, the total area of the received light waveform, and the background level.
The displacement sensor according to claim 5 . Among the multiple abnormality types in the abnormality type information pre-stored in the storage unit, abnormality types that can be determined depending on the presence or absence of a workpiece and a base in the inspection area are classified.
The displacement sensor according to claim 1 . The output unit outputs a countermeasure or an estimated cause corresponding to the determined abnormality type.
A displacement sensor according to any one of claims 1 to 7. 1. A condition monitoring method implemented by a displacement sensor including a processor, comprising:
a light projection step of projecting light onto an inspection area;
a light receiving step of receiving light reflected from the inspection area and outputting a received light waveform;
a feature information extracting step of extracting at least two types of feature information based on the received light waveform output in the light receiving step;
an abnormality type determination step of determining, based on the extracted at least two or more types of characteristic information, at least one or more types of abnormality among a plurality of abnormality types in abnormality type information, which is stored in advance in a memory and in which a plurality of pieces of characteristic information included in the received light waveform are associated with a plurality of abnormality types predicted from the plurality of pieces of characteristic information;
an output step of outputting the determined abnormality type;
A condition monitoring method comprising:

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