diagnostic device

Abstract

This diagnostic device includes a first acquisition unit that acquires first condition data indicating the condition of a diagnosis object, a normal diagnosis unit that diagnoses an abnormality in the diagnosis object based on the first condition data acquired by the first acquisition unit, a second acquisition unit that acquires second condition data different from the first condition data based on a diagnosis result obtained by the normal diagnosis unit, and an emergency diagnosis unit that diagnoses a case of an abnormality occurring in the diagnosis object based on the second condition data acquired by the second acquisition unit.

Description

[TECHNICAL FIELD]

[0001] The present disclosure relates to a diagnostic device for diagnosing an abnormality. [PREVIEW]

[0002] A technology is known to diagnose signs of abnormalities occurring in industrial machines by detecting and analyzing various physical quantities such as currents and vibrations (e.g., Patent Literature 1). [PRIOR ART DOCUMENT][PATENT LITERATURE]

[0003] [Patent Literature 1] Japanese Patent Application No. 2020-12691 [SUMMARY OF THE INVENTION][PROBLEMS TO BE SOLVED BY THE INVENTION]

[0004] However, collecting and analyzing a large amount of physical data leads to an increase in data management costs. For example, the large amount of physical data increases the burden on analysis processing. Furthermore, large amounts of physical data are collected, which do not make a significant contribution to the accuracy of fault diagnosis. On the other hand, with a small amount of data, fault diagnosis may not be possible with high accuracy. Therefore, there is a need for a diagnostic tool that can reduce the cost of fault diagnosis and ensure the reliability of fault diagnosis. [METHODS TO SOLVING THE PROBLEM]

[0005] A diagnostic device according to the present disclosure includes: a first acquisition unit that acquires first condition data indicating a condition of a diagnosis object; a normal diagnosis unit that diagnoses an abnormality in the diagnosis object based on the first condition data acquired by the first acquisition unit; a second acquisition unit that acquires second condition data different from the first condition data based on a diagnosis result obtained by the normal diagnosis unit; and an emergency diagnosis unit that diagnoses a cause of the abnormality occurred in the diagnosis object based on the second condition data acquired by the second acquisition unit. [BRIEF DESCRIPTION OF THE DRAWINGS] Fig. 1 shows an example of a system including a diagnostic device; Fig. 2 is a block diagram showing an example of a hardware configuration of an industrial machine; Fig. 3 is a block diagram showing an example of a hardware configuration of the diagnostic device; Fig. 4 is a block diagram showing an example of functions of the diagnostic device; Fig. 5 shows an example of a diagnostic result; Fig. 6 shows an example of a display screen showing information generated by a trigger generation unit; Fig. 7 shows an example of a reception screen; Fig. 8 shows an example of the diagnostic result displayed on a display device; Fig. 9 is a flowchart showing a processing procedure performed by the diagnostic device; Fig. 10 is a block diagram showing another example of the functions of the diagnostic device; Fig. 11 illustrates the cluster formation; Fig. 12 shows another example of the diagnostic result; and Fig. Figure 13 shows another example of the display screen on which the information generated by the trigger generation unit is shown. [MODE FOR IMPLEMENTING THE INVENTION]

[0006] A diagnostic device according to an embodiment of the present disclosure will now be described with reference to the accompanying drawings. In the following description, like reference numerals are used for components with the same or similar functions. These components may not be described repeatedly.

[0007] In this application, the term "based on XX" means "at least based on XX" and includes elements other than XX. Furthermore, the term "based on XX" is not limited to a case where XX is used directly, but also includes a case where calculations and / or processing are performed on XX. The word "XX" is any element (e.g., any information). <Erste Ausführungsform

[0008] Fig. Figure 1 shows an example of a system including a diagnostic device. This system includes, for example, an industrial machine 1 and a diagnostic device 10. The industrial machine 1 operates at an industrial site. The industrial machine 1 is, for example, a machine tool, an injection molding machine, a laser machine, a three-dimensional printer, and a robot. The industrial machine 1 is controlled by a controller 2.

[0009] The diagnostic device 10 is configured to diagnose an anomaly in the industrial machine 1. The diagnostic device 10 is connected to the controller 2 via a wired or wireless connection. The diagnostic device 10 can be implemented in the controller 2.

[0010] Fig. Figure 2 is a block diagram showing an example of a hardware configuration of the industrial machine 1. The industrial machine 1 includes the controller 2, an input / output device 3, a servo amplifier 4, a servo motor 5, a spindle amplifier 6, a spindle motor 7, an auxiliary device 8, and a sensor 9.

[0011] The controller 2 is, for example, a numerical controller configured to control the industrial machine 1. The controller 2 includes, for example, a hardware processor 201, a bus 202, a read-only memory (ROM) 203, a random-access memory (RAM) 204, and a non-volatile memory 205.

[0012] The hardware processor 201 is configured to control the entire controller 2 according to a system program. The hardware processor 201 reads a system program and other programs stored in the ROM 203 via the bus 202. The hardware processor 201 is, for example, a central processing unit (CPU) or an electronic circuit.

[0013] Bus 202 is a communication channel configured to connect parts of the hardware of controller 2. The parts of the hardware of controller 2 exchange data with each other via bus 202.

[0014] ROM 203 is a storage device configured to store the system program and other data. ROM 203 is a computer-readable storage medium.

[0015] RAM 204 is a memory device configured to temporarily store various data. RAM 204 serves as a workspace used by hardware processor 201 to process the various data.

[0016] Non-volatile memory 205 is a storage device configured to store data even when controller 2 is powered off. Non-volatile memory 205 stores, for example, an operating program of industrial machine 1. Non-volatile memory 205 is a computer-readable storage medium. Non-volatile memory 205 consists, for example, of a battery-backed memory or a solid-state drive (SSD).

[0017] The controller 2 further comprises a first interface 206, an axis control circuit 207, a spindle control circuit 208, a programmable logic controller (PLC) 209, an I / O unit 210, a second interface 211 and a third interface 212.

[0018] The first interface 206 connects the bus 202 to the input / output device 3. For example, the first interface 206 is configured to transmit the various data processed by the hardware processor 201 to the input / output device 3.

[0019] The input / output device 3 is configured to receive the various data via the first interface 206 and display the data on a display. Furthermore, after receiving the various input data, the input / output device 3 transmits the various data, for example, via the first interface 206, to the hardware processor 201.

[0020] The input / output device 3 is, for example, a touch panel. If the input / output device 3 is a touch panel, the input / output device 3 is, for example, a capacitive touch panel. The touch panel is not limited to the capacitive type and can be a touch panel of any other type. The input / output device 3 is installed on a control panel (not shown) in which the controller 2 is housed.

[0021] The axis control circuit 207 is configured to control the servo motor 5. The axis control circuit 207 receives control commands from the hardware processor 201 and sends various commands to the servo amplifier 4 to drive the servo motor 5. The axis control circuit 207 sends, for example, a torque command to control the torque of the servo motor 5 to the servo amplifier 4.

[0022] The servo amplifier 4 is configured to supply current to the servo motor 5 in response to commands from the axis control circuit 207.

[0023] The servo motor 5 is driven by the current supplied from the servo amplifier 4. The servo motor 5 is provided on each control axis of the industrial machine 1. If the industrial machine 1 is a five-axis machine tool, the servo motors 5 include, for example, an X-axis servo motor, a Y-axis servo motor, a Z-axis servo motor, an A-axis servo motor, and a C-axis servo motor. In this case, the axis control circuit 207 and the servo amplifier 4 are provided for each servo motor 5.

[0024] The servo motor 5 is coupled to a ball screw, which drives, for example, a tool carrier. The servo motor 5 is controlled such that a structure of the industrial machine 1, e.g., the tool carrier, can move along a predetermined control axis.

[0025] Servo motor 5 includes an encoder (not shown) configured to detect a control axis position and a feed rate. Position feedback information and speed feedback information indicating the control axis position and feed rate, respectively, detected by the encoder are fed back to the axis control circuit 207. This allows the axis control circuit 207 to perform feedback control for each control axis.

[0026] The spindle control circuit 208 is configured to control the spindle motor 7. The spindle control circuit 208 receives control commands from the hardware processor 201 and sends commands to drive the spindle motor 7 to the spindle amplifier 6. For example, the spindle control circuit 208 sends a spindle speed command to control a speed of the spindle motor 7 to the spindle amplifier 6.

[0027] The spindle amplifier 6 is configured to supply current to the spindle motor 7 in response to commands from the spindle control circuit 208.

[0028] The spindle motor 7 is driven by the current supplied by the spindle amplifier 6. The spindle motor 7 is coupled to a spindle to rotate it.

[0029] PLC 209 is configured to execute a ladder logic program to control auxiliary device 8. PLC 209 sends commands to auxiliary device 8 via I / O unit 210.

[0030] The I / O unit 210 is an interface configured to connect the PLC 209 to the auxiliary device 8. The I / O unit 210 transmits the commands from the PLC 209 to the auxiliary device 8.

[0031] The auxiliary device 8 is installed on the industrial machine 1 and configured to perform auxiliary functions in the industrial machine 1. The auxiliary device 8 operates based on the commands received from the I / O unit 210. The auxiliary device 8 can be arranged on the periphery of the industrial machine 1. The auxiliary device 8 is, for example, a turret, a coolant injection device, or a door opening / closing drive unit.

[0032] The second interface 211 connects the bus 202 to the sensor 9. For example, the second interface 211 is configured to transmit information detected by the sensor 9 to the hardware processor 201 via the bus 202.

[0033] Sensor 9 is provided on each component of industrial machine 1 to detect various physical quantities. Sensor 9 may be, for example, a temperature sensor, an acceleration sensor, an ammeter, and a liquid level sensor.

[0034] The third interface 212 connects the bus 202 to the diagnostic device 10. For example, the third interface 212 is configured to transmit information processed by the hardware processor 201 to the diagnostic device 10 via the bus 202.

[0035] Fig. 3 is a block diagram showing an example of a hardware configuration of the diagnostic device 10. The diagnostic device 10 includes, for example, a hardware processor 101, a bus 102, a ROM 103, a RAM 104, a non-volatile memory 105, a first interface 106, and a second interface 107.

[0036] The hardware processor 101 is configured to control the entire diagnostic device 10 according to a system program. The hardware processor 101 reads a system program and other data stored in the ROM 103 via the bus 102. The hardware processor 101 is, for example, a CPU or an electronic circuit.

[0037] Bus 102 is a communication channel configured to interconnect parts of the hardware of diagnostic device 10. The parts of the hardware of diagnostic device 10 exchange data with each other via bus 102.

[0038] ROM 103 is a storage device configured to store the system program and other information. ROM 103 is a computer-readable storage medium.

[0039] RAM 104 is a memory device configured to temporarily store various data. RAM 104 serves as a workspace used by hardware processor 101 to process the various data.

[0040] Non-volatile memory 105 is a storage device configured to store data even when diagnostic device 10 is turned off. Non-volatile memory 105 is a computer-readable storage medium. Non-volatile memory 105 consists, for example, of a battery-backed memory or an SSD.

[0041] The first interface 106 connects the bus 102 to an input / output device 11. For example, the first interface 106 is configured to transmit various data processed by the hardware processor 101 to the input / output device 11.

[0042] The input / output device 11 is configured to receive the various data via the first interface 106 and display the data on a display. Furthermore, after receiving the various input data, the input / output device 11 transmits the various data via the first interface 106, for example, to the hardware processor 101. The input / output device 11 is, for example, a touch panel.

[0043] The second interface 107 connects the bus 102 to the controller 2. For example, the second interface 107 is configured to transmit the various data received from the controller 2 to the hardware processor 101.

[0044] Fig. 4 is a block diagram showing an example of the functions of the diagnostic device 10. For example, the diagnostic device 10 includes a first acquisition unit 111, a first data storage unit 112, a normal diagnosis unit 113, a first history storage unit 114, a trigger generation unit 115, a result output unit 116, a receiving unit 117, a second acquisition unit 118, a second data storage unit 119, an emergency diagnosis unit 120, and a second history storage unit 121.

[0045] The first detection unit 111, the normal diagnosis unit 113, the trigger generation unit 115, the result output unit 116, the receiving unit 117, the second detection unit 118, and the emergency diagnosis unit 120 are executed by arithmetic operations of the hardware processor 101 using the system program stored in the ROM 103 and the various data stored in the non-volatile memory 105.

[0046] For example, the first data storage unit 112, the first history storage unit 114, the second data storage unit 119, and the second history storage unit 121 are implemented by storing various information in the RAM 104 or the non-volatile memory 105.

[0047] The first acquisition unit 111 is configured to acquire first status data indicating a status of a diagnostic object. The diagnostic object includes, for example, devices and components that constitute the industrial machine 1. The devices and components that constitute the industrial machine 1 include, for example, a bearing, the servo motor 5, the spindle motor 7, a linear guide, a tool, and a spindle. The diagnostic object may be a compressor, a motor, or similar equipment installed in a factory.

[0048] The first acquisition unit 111 acquires the first status data in a predetermined cycle when the diagnostic object is operating. The meaning of "when the diagnostic object is operating" is, for example, a period of time during which the industrial machine 1 is operating according to an operating program.

[0049] The predetermined cycle is, for example, a control cycle of the diagnostic device 10. The first acquisition unit 111 may change the cycle for acquiring the first state data. For example, the first acquisition unit 111 may acquire the first state data based on information indicating a cycle accepted by the receiving unit 117, which will be described later. More specifically, the first acquisition unit 111 may acquire the first state data at a cycle specified by an operator.

[0050] The first state data includes, for example, a torque command and data on the current value, vibration, temperature, noise, elastic waves, speed, and rotational speed. The data includes a signal. The first acquisition unit 111 acquires, for example, a torque command indicating the torque of the spindle motor 7.

[0051] The first acquisition unit 111 acquires the first status data from the sensor 9 installed on the diagnostic object or from the controller 2.

[0052] The first data storage unit 112 is configured to store the first state data acquired by the first acquisition unit 111. The first data storage unit 112 stores the first state data in association with time information indicating a time point at which the first state data was acquired. That is, the first state data stored in the first data storage unit 112 is time series data. When the first acquisition unit 111 acquires the torque command indicating the torque of the spindle motor 7, the data stored in the first data storage unit 112 is time series data of values ​​indicating the torque of the spindle motor 7.

[0053] The normal diagnosis unit 113 is configured to diagnose an abnormality in the diagnosis object based on the first state data stored in the first data storage unit 112. That is, the normal diagnosis unit 113 diagnoses the abnormality in the diagnosis object based on the first state data acquired by the first acquisition unit 111.

[0054] For example, the normal diagnosis unit 113 diagnoses whether an abnormality exists in the diagnosis object. The normal diagnosis unit 113 diagnoses the abnormality in the diagnosis object using a predefined abnormality detection model.

[0055] For example, the anomaly detection model diagnoses the occurrence of an anomaly in the diagnostic object if the value in the initial state data exceeds a predefined threshold. Multiple thresholds can be defined for the anomaly detection model.

[0056] For example, the anomaly detection model can specify a first threshold, a second threshold greater than the first threshold, and a third threshold greater than the second threshold. In this case, the anomaly detection model calculates an anomaly degree.

[0057] For example, if the value in the first set of status data is equal to or less than the first threshold, the anomaly level is "0." In this case, no anomaly exists in the diagnostic object. If the value in the first set of status data exceeds the first threshold and is equal to or less than the second threshold, the anomaly level is "1." In this case, a minor anomaly exists in the diagnostic object.

[0058] If the value in the first state data exceeds the second threshold and is equal to or less than the third threshold, the anomaly level is "2." In this case, an anomaly of medium significance exists in the diagnosis object. If the value in the first state data exceeds the third threshold, the anomaly level is "3." In this case, an anomaly of high significance exists in the diagnosis object.

[0059] Fig. Figure 5 shows an example of a diagnosis result obtained by the normal diagnosis unit 113. The value in the first state data is equal to or less than the first threshold during periods T1 and T3. Therefore, the normal diagnosis unit 113 determines that the diagnosis object is functioning normally.

[0060] The value in the first state data during a period T2 exceeds the first threshold and is equal to or less than the second threshold. In this case, the normal diagnosis unit 113 determines that an abnormality of abnormality level "1" exists in the diagnosis object. Furthermore, the value in the first state data during a period T4 exceeds the second threshold. In this case, the normal diagnosis unit 113 determines that an abnormality of abnormality level "2" exists in the diagnosis object.

[0061] The normal diagnosis unit 113 uses an abnormality detection model selected from a plurality of abnormality detection models to calculate the degree of abnormality and diagnose the abnormality in the diagnosis object. Specifically, the abnormality detection model selected from the plurality of abnormality detection models best calculates the degree of abnormality occurring in the diagnosis object.

[0062] For example, a difference between an abnormality degree in a normal state and an abnormality degree in an abnormal state calculated by the abnormality detection model used by the normal diagnosis unit 113 is larger than differences between the abnormality degree in the normal state and the abnormality degree in the abnormal state calculated by other abnormality detection models among the plurality of abnormality detection models.

[0063] The anomaly detection model is selected from the plurality of anomaly detection models according to the elements constituting the diagnosis object, the configuration of the industrial machine 1 in which the diagnosis object is installed, the setting status of the industrial machine 1, an operation program for operating the industrial machine 1, and the environment in which the diagnosis object is installed.

[0064] For example, the setting status of industrial machine 1 means the pressurization of a bearing. Furthermore, the setting status of industrial machine 1 includes a setting status of parameters set in controller 2. The environment in which the diagnostic object is installed means a temperature in the factory in which industrial machine 1 is installed. Now, the description of Fig. 4 given again.

[0065] The first history storage unit 114 is configured to store a diagnosis result obtained by the normal diagnosis unit 113. The diagnosis result is, for example, information indicating whether an abnormality exists in the diagnosis object. The diagnosis result may include information indicating the degree of the abnormality. The diagnosis result may include information indicating the time of occurrence of the abnormality.

[0066] The trigger generation unit 115 is configured to generate a trigger based on the diagnosis result stored in the first history storage unit 114. The trigger generation unit 115 generates a trigger in response to storing the diagnosis result indicating the occurrence of the abnormality in the diagnosis object in the first history storage unit 114.

[0067] The trigger is information or a signal that leads to the acquisition of second status data, which differs from the first status data, by the second acquisition unit 118. The information generated by the trigger generation unit 115 serves, for example, to prompt the operator to perform a diagnostic procedure. The information prompting the execution of the diagnostic procedure is displayed, for example, on the display device.

[0068] Fig. Figure 6 shows an example of a display screen showing the information generated by the trigger generation unit 115. The display screen shows, for example, the text "Error occurred. Please perform a diagnostic procedure." An "OK" button appears below.

[0069] When the information generated by the trigger generation unit 115 is displayed on the display screen, the operator executes the diagnostic operation. For example, if the initial status data is a torque command for the spindle motor 7 and the normal diagnostic unit 113 diagnoses an abnormality in the diagnostic object, the operator executes the diagnostic operation on the spindle.

[0070] The diagnostic process, for example, is measuring spindle runout. Spindle runout is measured, for example, by measuring the distance between a tool holder attached to the spindle and an eddy current displacement sensor while the spindle is rotating. Spindle runout can be measured by attaching a dial indicator to the tool holder attached to the spindle.

[0071] The diagnostic process can be a measurement of the vibrations in the diagnostic object. The vibrations in the diagnostic object are, for example, vibrations that occur when the diagnostic object undergoes a frequency sweep process and vibrations that occur when the diagnostic object is excited. This means that the data of the second state can be vibration data acquired when the diagnostic object undergoes the frequency sweep process or vibration data acquired when the diagnostic object is excited.

[0072] Frequency sweeping is used to oscillate a motor, such as the servo motor 5 and the spindle motor 7, using an input signal input to the motor. For example, frequency sweeping gradually increases the frequency of the input signal to the motor. Excitation of the diagnostic object means, for example, subjecting the diagnostic object to a shock from an impact hammer or similar device.

[0073] The result output unit 116 is configured to output the diagnosis result obtained from the normal diagnosis unit 113. For example, in response to the storage of the diagnosis result in the first history storage unit 114 indicating that the diagnosis object is functioning normally, the result output unit 116 displays a diagnosis result indicating that the diagnosis object is functioning normally, for example, on a display device.

[0074] On the other hand, the result output unit 116 displays, for example, in response to the storage of the diagnosis result in the first history storage unit 114 indicating the occurrence of an abnormality in the diagnosis object, a diagnosis result indicating that the abnormality exists in the diagnosis object on the display device. The display device is, for example, the input / output device 11. The result output unit 116 can, for example, output an electronic file in which the diagnosis result is recorded to an external server (not shown). Now, the description of Fig. 4 given again.

[0075] The receiving unit 117 is configured to accept the diagnostic operation result input by the operator. For example, the receiving unit 117 displays a receiving screen on the display device and accepts the input of the diagnostic operation result from the receiving screen.

[0076] Fig. Figure 7 shows an example of the receiving screen. The receiving screen includes, for example, an area for receiving an input of a measured value of the spindle runout and an area for receiving an input of a measured value of the vibration in the spindle. After the operator enters the measured values ​​in these areas, the receiving unit 117 accepts the result of the diagnostic process. The description of Fig. 4 is now given again.

[0077] The second acquisition unit 118 is configured to acquire second condition data that differs from the first condition data based on the diagnosis result obtained by the normal diagnosis unit 113. The second condition data is, for example, information indicating the result of the diagnosis operation, which was input by the operator and accepted by the receiving unit 117. In other words, when a trigger is generated based on the diagnosis result obtained by the normal diagnosis unit 113, the second acquisition unit 118 acquires information indicating the result of the diagnosis operation performed based on the trigger.

[0078] The second acquisition unit 118 acquires, for example, information indicating spindle runout and information indicating vibration in the spindle. The second state data is not limited to the information accepted by the receiving unit 117. The second state data may be data acquired by the sensor 9. In this case, the second acquisition unit 118 begins acquiring the second state data in a predetermined cycle in response to the generation of the trigger by the trigger generation unit 115.

[0079] The data acquired by sensor 9 includes, for example, the torque command as well as various data on the current value, vibration, temperature, sound, elastic wave, speed, and rotational speed. The data includes signals. The second state data can be acquired by controller 2.

[0080] The second detection unit 118 can, for example, detect information about the electrical resistance between a workpiece and a tool during cutting from the sensor 9. It is known that as the tool wears, the electrical resistance between the tool and the workpiece decreases. Since the second detection unit 118 detects the information indicating the electrical resistance between the tool and the workpiece, the emergency diagnostic unit 120 described later can diagnose the tool wear based on the information indicating the electrical resistance.

[0081] Furthermore, the second detection unit 118 can detect, for example, the temperature of the bearing from the sensor 9. It is known that the temperature of the bearing rises when the bearing is damaged. Since the second detection unit 118 detects the temperature of the bearing, the emergency diagnostic unit 120 described below can diagnose the damage to the bearing based on this temperature.

[0082] The second data storage unit 119 is configured to store the second state data acquired by the second acquisition unit 118. That is, the second data storage unit 119 stores the information accepted by the receiving unit 117.

[0083] The second data storage unit 119 also stores the data acquired by the sensor 9. If the second state data is the data acquired by the sensor 9, the second data storage unit 119 stores the second state data in conjunction with time information indicating a point in time at which the second state data was acquired. In other words, the second state data stored in the second data storage unit 119 may be time series data.

[0084] The emergency diagnosis unit 120 is configured to diagnose the cause of the abnormality occurring in the diagnosis object based on the second state data acquired by the second acquisition unit 118. In other words, the emergency diagnosis unit 120 diagnoses the cause of the abnormality occurring in the diagnosis object based on the second state data stored in the second data storage unit 119.

[0085] The emergency diagnosis unit 120 uses a predefined diagnosis model to diagnose the cause of the abnormality occurring in the diagnosis object. The cause of the abnormality refers, for example, to a location where the abnormality occurs and an abnormal state.

[0086] For example, the emergency diagnostic unit 120 diagnoses the cause of the anomaly as being an anomaly in the bearing, motor, or tool. The emergency diagnostic unit 120 also diagnoses, for example, wear, loss, or breakage. Furthermore, the emergency diagnostic unit 120 diagnoses that a component has reached the end of its service life.

[0087] The diagnostic model shows, by way of example, a correlation between the second state data and the cause of the anomaly occurring in the diagnostic object. The diagnostic model is created by performing machine learning using the second state data and the information indicating the cause of the anomaly as training data.

[0088] For example, if the measured value of the spindle runout is equal to or greater than α1 [mm] and less than α2 [mm], and vibration occurs in a specific frequency band during spindle rotation, the diagnostic model outputs information indicating that the spindle bearing is damaged. In other words, the emergency diagnostic unit 120 diagnoses that the spindle bearing is damaged. Here, the vibration in the specific frequency band includes, for example, a sideband wave of a shaft, which indicates a rotation frequency of the spindle.

[0089] For example, if the measured value of the spindle runout is equal to or greater than α2 [mm] and the vibration occurs in the specific frequency band while the spindle is rotating, the diagnostic model outputs information indicating that a foreign object is located between the spindle and the tool holder. In other words, the emergency diagnostic unit 120 diagnoses that the foreign object is located between the spindle and the tool holder. Here, the vibration in the specific frequency band represents, for example, the rotation frequency of the spindle.

[0090] When the second state data includes multiple data types, the emergency diagnosis unit 120 can diagnose the cause of the abnormality occurred in the diagnosis object based on the correlation between the multiple data types. For example, if there is a correlation between a rotational speed of the servo motor 5 in the normal state and a frequency component of vibration in the servo motor 5 in the normal state, a correlation between a rotational speed of the servo motor 5 in the abnormal state and the frequency component of the vibration is different from the correlation in the normal state. Thus, the emergency diagnosis unit 120 diagnoses the cause of the abnormality in the diagnosis object based on the fact that the correlation between the rotational speed of the servo motor 5 and the frequency component of the vibration is different from the correlation between the rotational speed of the servo motor 5 in the normal state and the frequency component of the vibration.

[0091] The second history storage unit 121 is configured to store a history of cause information indicating the cause of the abnormality in the diagnosis object diagnosed by the emergency diagnosis unit 120. The cause information is stored in association with the second state data and the time of acquisition of the second state data, respectively.

[0092] The emergency diagnosis unit 120 can diagnose the cause of the abnormality occurring in the diagnosis object based on the cause information stored in the second history storage unit 121. Specifically, the emergency diagnosis unit 120 determines whether the second state data stored in the second history storage unit 121 matches the second state data stored in the second data storage unit 119. If these data match, the emergency diagnosis unit 120 diagnoses that the abnormality indicated by the cause information stored in association with the second state data in the second history storage unit 121 exists in the diagnosis object.

[0093] The previous second state data stored in the second history storage unit 121 may not necessarily exactly match the second state data stored in the second data storage unit 119. That is, if the previous second state data is similar to the newly acquired second state data within a predefined range, the emergency diagnosis unit 120 may diagnose that the cause information associated with the second state data indicates an abnormality in the diagnosis object.

[0094] The result output unit 116 is configured to output the diagnosis result obtained from the emergency diagnosis unit 120. The result output unit 116 displays the diagnosis result on the display device, for example, in response to the storage of the diagnosis result indicating the cause of the abnormality in the second history storage unit 121.

[0095] Fig. 8 shows an example of the diagnostic result displayed on the display device. For example, in response to the storage of a diagnostic result indicating the occurrence of an abnormality in the spindle bearing in the second history storage unit 121, the result output unit 116 displays a text string indicating the diagnostic result "There is a high probability of an abnormality in the spindle bearing" on the display device. The result output unit 116 can display the diagnostic result obtained from the emergency diagnostic unit 120 together with the diagnostic result obtained from the normal diagnostic unit 113 on the display device. The result output unit 116 can, for example, output an electronic file in which the diagnostic result is recorded to the external server (not shown).

[0096] Fig. 9 is a flowchart showing an example of a processing sequence performed by the diagnostic device 10. First, the first acquisition unit 111 in the diagnostic device 1 acquires the first state data (step SA1).

[0097] Subsequently, the first data storage unit 112 stores the first state data acquired by the first acquisition unit 111 (step SA2).

[0098] Subsequently, the normal diagnosis unit 113 diagnoses an abnormality in the diagnosis object based on the first state data stored in the first data storage unit 112 (step SA3).

[0099] If there is no abnormality in the diagnosis object (No in step SA4), the first acquisition unit 111 continues to acquire the first state data (step SA1).

[0100] If there is an abnormality in the diagnosis object (Yes in step SA4), the first history storage unit 114 stores the diagnosis result obtained from the emergency diagnosis unit 113 (step SA5).

[0101] When the diagnosis result is stored in the first history storage unit 114, the trigger generation unit 115 generates a trigger and the result output unit 116 outputs the diagnosis result (step SA6).

[0102] When the trigger generation unit 115 generates the trigger, the operator performs a diagnosis operation, and the receiving unit 117 accepts the input of a result of the diagnosis operation (step SA7).

[0103] In the next step, the second acquisition unit 118 acquires second status data (step SA8). The second status data is information indicating the result of the diagnostic process accepted by the receiving unit 117.

[0104] Subsequently, the second data storage unit 119 stores the second state data acquired by the second acquisition unit 118 (step SA9).

[0105] Thereafter, the emergency diagnosis unit 120 diagnoses the cause of the abnormality occurred in the diagnosis object based on the second state data stored in the second data storage unit 119 (step SA10).

[0106] Subsequently, the second history storage unit 121 stores the information indicating the cause of the abnormality diagnosed by the emergency diagnosis unit 120 (step SA11).

[0107] Finally, the result output unit 116 outputs the information indicating the cause of the abnormality stored in the second history storage unit 121 (step SA12), and the processing is terminated. <Zweite Ausführungsform>

[0108] Fig. 10 is a block diagram showing an example of the functions of the diagnostic device 10 according to a second embodiment. The following mainly describes the functions that are different from those according to the first embodiment, and descriptions of the same functions as those in the first embodiment may be omitted.

[0109] The diagnostic device 10 of the present embodiment differs from the diagnostic device 10 of the first embodiment in that the normal diagnostic unit 113 includes a first diagnostic unit 113A and a second diagnostic unit 113B.

[0110] The normal diagnosis unit 113 includes the first diagnosis unit 113A and the second diagnosis unit 113B. The first diagnosis unit 113A is configured to diagnose whether an abnormality exists in a diagnosis object based on first state data. The first diagnosis unit 113A uses a predefined abnormality detection model to diagnose whether the abnormality exists in the diagnosis object.

[0111] For example, the anomaly detection model diagnoses the occurrence of the anomaly when the value of the first state data exceeds a preset threshold.

[0112] The second diagnostic unit 113B is configured to diagnose the type of anomaly occurring in the diagnosis object based on the first state data. The second diagnostic unit 113B performs clustering of the first state data to diagnose the type of anomaly.

[0113] Fig. Figure 11 illustrates the cluster formation. Fig. The black circles shown in Figure 11 indicate diagnostic data obtained by diagnosing the first state data. The diagnostic data is, for example, multidimensional data obtained by frequency diagnosis of the first state data.

[0114] The second diagnostic unit 113B uses a clustering technique, such as k-means clustering, to group the diagnostic data. For example, the second diagnostic unit 113B groups the diagnostic data into a first group G1, a second group G2, and a third group G3.

[0115] Each group is labeled with information indicating the anomaly. For example, if a group contains diagnostic data from the first state data acquired when a known anomaly occurred, this group is assumed to be a cluster of diagnostic data from the first state data acquired when the known anomaly occurred. This means that this group is labeled with information indicating the known anomaly.

[0116] It is assumed that the diagnostic data D1 is derived by diagnosing the first state data acquired when the diagnostic object was operating in a normal state. In this case, other diagnostic data included in the first group is assumed to be derived by diagnosing the first state data acquired when the diagnostic object was operating in a normal state. Therefore, the first group is labeled with information indicating that the diagnostic object is in a normal state.

[0117] It is assumed that the diagnostic data D2 is derived by diagnosing the first condition data acquired when the diagnostic object was operating in a worn condition. In this case, it is assumed that other diagnostic data included in the second group is derived by diagnosing the first condition data acquired when the diagnostic object was operating in a worn condition. Therefore, the second group is labeled with information indicating that the diagnostic object is worn.

[0118] It is assumed that the diagnostic data D3 is derived by diagnosing the first state data acquired when the diagnostic object was operating in a damaged state. In this case, other diagnostic data included in the third group is assumed to be derived by diagnosing the first state data acquired when the diagnostic object was operating in the damaged state. Thus, the third group is labeled with information indicating that the diagnostic object is damaged.

[0119] The second diagnostic unit 113B determines which group the diagnostic data derived by diagnosing the first state data belongs to. For example, if the diagnostic data belongs to the first group G1, the second diagnostic unit 113B diagnoses that the diagnostic object is functioning normally.

[0120] If the diagnostic data belongs to the second group G2, the second diagnostic unit 113B diagnoses that the diagnostic object is operating in a worn state. That is, the second diagnostic unit 113B diagnoses that the type of abnormality occurring in the diagnostic object is wear.

[0121] If the diagnosis data belongs to the third group G3, the second diagnosis unit 113B diagnoses that the diagnosis object is operating in a damaged state. That is, the second diagnosis unit 113B diagnoses that the abnormality occurring in the diagnosis object is damage.

[0122] If the diagnosis data does not belong to a group, the second diagnosis unit 113B diagnoses that there is an unknown abnormality in the diagnosis object.

[0123] Fig. 12 shows an example of the diagnosis result obtained by the normal diagnosis unit 113. If the value of the first state data is equal to or lower than the predefined threshold, it is determined that the diagnosis object is functioning normally. If the value of the first state data exceeds the predefined threshold, the first diagnosis unit 113A diagnoses that an abnormality exists in the diagnosis object. In other words, the first diagnosis unit 113A diagnoses that an abnormality exists in the diagnosis object at time T2 and time T4.

[0124] The second diagnostic unit 113B diagnoses the type of abnormality that has occurred in the diagnostic object based on the diagnostic data obtained by diagnosing the first condition data. For example, the second diagnostic unit 113B diagnoses that the diagnostic object is worn out at time T2. Furthermore, the second diagnostic unit 113B diagnoses that the diagnostic object is damaged at time T4.

[0125] The first history storage unit 114 stores the diagnosis result obtained from the normal diagnosis unit 113. The trigger generation unit 115 generates a trigger based on the diagnosis result stored in the first history storage unit 114. The trigger generation unit 115 generates the trigger in response to storing the diagnosis result indicating the occurrence of an abnormality in the diagnosis object in the first history storage unit 114.

[0126] Fig.Figure 13 shows an example of a display screen showing information generated by the trigger generation unit 115. The display screen displays, for example, the text "An unknown anomaly has occurred. Please perform a diagnosis." Furthermore, an "OK" button appears below the text.

[0127] When the information generated by the trigger generation unit 115 is displayed on the display screen, the operator performs the diagnostic operation.

[0128] The result output unit 116 outputs the diagnosis result obtained by the normal diagnosis unit 113. In this embodiment, the functions of the receiving unit 117, the second acquisition unit 118, the second data storage unit 119, the emergency diagnosis unit 120, and the second history storage unit 121 are the same as in the first embodiment.

[0129] The receiving unit 117 can receive information indicating the type of the first state data. In this case, the operator can specify the type of the first state data to be used for diagnosis in the normal diagnosis unit 113. Furthermore, the receiving unit 117 can receive information indicating a fault detection model. In this case, multiple abnormality detection models can be stored in advance in a storage unit (not shown). The first diagnosis unit 113A can diagnose the abnormality in the diagnosis object using a specific abnormality detection model.

[0130] The second acquisition unit 118 may acquire the diagnosis result obtained from the normal diagnosis unit 113. In other words, the second acquisition unit 118 may acquire the diagnosis result stored in the first history storage unit 114. In this case, the emergency diagnosis unit 120 diagnoses the cause of the abnormality that occurred in the diagnosis object based on the diagnosis result obtained from the normal diagnosis unit 113 and the second state data.

[0131] The second acquisition unit 118 may determine the type of second condition data to be acquired according to the diagnostic result obtained from the normal diagnostic unit 113. For example, the second acquisition unit 118 may acquire either information indicating spindle runout or information indicating spindle vibration, depending on whether the diagnostic result is wear or damage.

[0132] As described above, the diagnostic device 10 includes the first acquisition unit 111 for acquiring the first state data indicating the state of the diagnosis object, a normal diagnosis unit 113 for diagnose the abnormality in the diagnosis object based on the first state data acquired by the first acquisition unit 111, a second acquisition unit 118 for acquiring the second state data different from the first state data based on the diagnosis result obtained by the normal diagnosis unit 113, and an emergency diagnosis unit 120 for diagnose the cause of the abnormality occurred in the diagnosis object based on the second state data acquired by the second acquisition unit 118.

[0133] In this way, the diagnostic device 10 can diagnose the diagnostic object based on the initial state data that can be acquired at any time during the operation of the diagnostic object. That is, the diagnostic device 10 does not affect the operation of the diagnostic object when diagnosing the diagnostic object.

[0134] The second acquisition unit 118 acquires the second condition data only when the normal diagnosis unit 113 deems it necessary. In other words, the second acquisition unit 118 acquires the second condition data at an appropriate time. Consequently, the diagnostic device 10 can reduce the cost of diagnosing the abnormality.

[0135] The diagnostic device 10 diagnoses the diagnostic object not only based on the first state data, but also based on the second state data. In this way, the diagnostic device 10 can ensure the reliability of the diagnostic result.

[0136] Furthermore, the normal diagnosis unit 113 diagnoses the abnormality in the diagnosis object using an abnormality detection model selected from a plurality of abnormality detection models to calculate an abnormality degree. If the difference between the abnormality degree in a normal state and the abnormality degree in an abnormal state calculated by the abnormality detection model is greater than the difference between the abnormality degree in the normal state and the abnormality degree in the abnormal state calculated by another normal detection model from the plurality of abnormality detection models, the normal diagnosis unit 113 can appropriately diagnose the abnormality in the diagnosis object.

[0137] The abnormality detection model is selected from among the multiple abnormality detection models according to the elements constituting the diagnosis object, the configuration of the industrial machine 1 in which the diagnosis object is installed, the setting status of the industrial machine 1, the operating program for operating the industrial machine 1, and the environment in which the diagnosis object is installed. In this way, the appropriate fault detection model for fault detection can be determined. Consequently, the normal diagnosis unit 113 can appropriately diagnose the abnormality in the diagnosis object.

[0138] The diagnostic device 10 further includes the receiving unit 117, which accepts at least one of the information specifying the type of the first state data and the information specifying the type of the fault detection model. In this way, the diagnostic device 10 can appropriately diagnose the cause of the abnormality occurring in the diagnosis object based on the first state data and the abnormality detection model suitable for the diagnosis object.

[0139] The first acquisition unit 111 also changes the cycle for acquiring the first condition data. This allows the operator to adjust the cycle according to the throughput of the diagnostic device 10.

[0140] The second condition data is vibration data acquired when the diagnostic object undergoes a frequency sweep or vibration data acquired when the diagnostic object is excited. Thus, the diagnostic device 10 can diagnose the cause of the abnormality occurring in the diagnostic object based on the vibration data.

[0141] The emergency diagnostic unit 120 also diagnoses the cause of the abnormality by diagnosing a frequency component of the second state data. In this way, the emergency diagnostic unit 120 can diagnose the cause of the abnormality occurring in the diagnosis object with high accuracy.

[0142] Furthermore, the second state data includes various data, and the emergency diagnosis unit 120 diagnoses the cause of the abnormality occurring in the diagnosis object based on a correlation between the various data. In this way, the emergency diagnosis unit 120 can diagnose the cause of the abnormality occurring in the diagnosis object with high accuracy.

[0143] The diagnostic device 10 further includes a history storage unit that records a history of cause information indicating the cause of the abnormality diagnosed by the emergency diagnostic unit 120. The emergency diagnostic unit 120 diagnoses the cause based on the cause information recorded in the history storage unit. In this way, the emergency diagnostic unit 120 diagnoses the cause of the abnormality occurring in the diagnosis object based on a history of previous abnormalities. This allows the diagnostic device 10 to reduce the processing load for fault diagnosis. The history storage unit is, for example, the second history storage unit 121.

[0144] The normal diagnosis unit 113 includes the first diagnosis unit 113A and the second diagnosis unit 113B. The first diagnosis unit 113A diagnoses whether an abnormality exists in the diagnosis object, and the second diagnosis unit 113B diagnoses the type of abnormality occurring in the diagnosis object. Thus, the emergency diagnosis unit 120 can select the second state data to be used to diagnose the cause of the abnormality according to the type of abnormality diagnosed by the second diagnosis unit 113B.

[0145] The present disclosure has been described in detail above, but is not limited to the individual embodiments described above. Therefore, various additions, substitutions, modifications, partial deletions, etc., may be made to these embodiments without departing from the spirit of the disclosure as understood from the appended claims and their equivalents. Furthermore, these embodiments may be implemented by combining them with one another.

[0146] With respect to the embodiments described above, additional remarks are disclosed below. Supplementary note (1)

[0147] A diagnostic device includes a first acquisition unit that acquires first condition data indicating a condition of a diagnosis object, a normal diagnosis unit that diagnoses an abnormality in the diagnosis object based on the first condition data acquired by the first acquisition unit, a second acquisition unit that acquires second condition data different from the first condition data based on a diagnosis result obtained by the normal diagnosis unit, and an emergency diagnosis unit that diagnoses a cause of the abnormality occurring in the diagnosis object based on the second condition data acquired by the second acquisition unit. Supplementary note (2)

[0148] The diagnostic device according to Supplementary Note (1), wherein the normal diagnosis unit diagnoses the abnormality in the diagnosis object using an abnormality detection model selected from a plurality of abnormality detection models for calculating a degree of the abnormality, and a difference between a degree of the abnormality in a normal state and a degree of the abnormality in an abnormal state calculated by the abnormality detection model is larger than a difference between the degree of the abnormality in the normal state and the degree of the abnormality in the abnormal state calculated by another anomaly detection model from the plurality of anomaly detection models. Supplementary note (3)

[0149] The diagnostic device according to Supplementary Note (2), in which the abnormality detection model is selected from a plurality of abnormality detection models based on elements constituting the diagnosis object, a configuration of an industrial machine in which the diagnosis object is installed, a setting status of the industrial machine, an operation program for operating the industrial machine, and an environment in which the diagnosis object is installed. Supplementary note (4)

[0150] The diagnostic device according to Supplementary Note (2) or (3) further comprises a receiving unit that accepts at least one of information specifying the type of the first state data and information specifying the type of the anomaly detection model. Supplementary note (5)

[0151] The diagnostic device according to any one of Supplementary Notes (1) to (4), wherein the first acquisition unit changes a cycle for acquiring the first state data. Supplementary note (6)

[0152] The diagnostic device according to any one of Supplementary Notes (1) to (5), wherein the second state data is vibration data acquired when the diagnostic object is subjected to a frequency sweep or vibration data acquired when the diagnostic object is excited. Supplementary note (7)

[0153] The diagnostic device according to any one of Supplementary Notes (1) to (6), wherein the emergency diagnostic unit diagnoses a frequency component of the second state data to diagnose the cause of the abnormality. Supplementary Note (8)

[0154] The diagnostic device according to any one of Supplementary Notes (1) to (7), wherein the second state data includes various data, and the emergency diagnostic unit diagnoses the cause of the abnormality occurring in the diagnosis object based on a correlation between the various data. Supplementary note (9)

[0155] The diagnostic device according to any one of Supplementary Notes (1) to (8) further comprises a history storage unit that records a history of cause information indicating the cause of the abnormality diagnosed by the emergency diagnosis unit, wherein the emergency diagnosis unit diagnoses the cause based on the cause information recorded in the history storage unit. Supplementary Note (10)

[0156] The diagnostic device according to any one of Supplementary Notes (1) to (9), wherein the normal diagnostic unit comprises a first diagnostic unit and a second diagnostic unit, wherein the first diagnostic unit diagnoses whether an abnormality occurs in the diagnosis object, and the second diagnostic unit diagnoses the type of abnormality occurring in the diagnosis object. [LIST OF REFERENCE SYMBOLS] 1 Industrial Machine 2 Control 201 hardware processor 202 Bus 203 ROM 204 RAM 205 Non-volatile memory 206 First interface 207 Axis control circuit 208 Spindle control circuit 209 PLC 210 I / O unit 211 Second interface 212 Third interface 4 servo amplifiers 5 Servo motor 6 spindle amplifiers 7 spindle motor 8 Auxiliary device 9 Sensor 10 diagnostic device 101 Hardware Processor 102 buses 103 ROM 104 RAM 105 Non-volatile memory 106 First interface 107 Second interface 111 First registration unit 112 First data storage unit 113 Normal Diagnostic Unit 113A First diagnostic unit 113B Second diagnostic unit 114 First history storage unit 115 Trigger generation unit 116 Unit for outputting results 117 Receiving unit 118 Second recording unit 119 Second data storage unit 120 Emergency Diagnostic Unit 121 Second history storage unit 11 Input / output unit QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] JP 2020-12691

[0003]

Claims

[1] A diagnostic device comprising: a first acquisition unit that acquires first state data indicating a state of a diagnosis object; a normal diagnosis unit that diagnoses an abnormality in the diagnosis object based on the first state data acquired by the first acquisition unit; a second acquisition unit that acquires second condition data different from the first condition data based on a diagnosis result obtained by the normal diagnosis unit; and an emergency diagnosis unit that diagnoses a cause of the abnormality occurred in the diagnosis object based on the second state data acquired by the second acquisition unit. [2] The diagnostic device according to claim 1, wherein the normal diagnosis unit diagnoses the abnormality in the diagnosis object by using an abnormality detection model selected from a plurality of abnormality detection models for calculating an abnormality degree, and a difference between an abnormality degree in a normal state and an abnormality degree in an abnormal state calculated by the abnormality detection model is larger than a difference between the abnormality degree in the normal state and the abnormality degree in the abnormal state calculated by other anomaly detection models among the plurality of anomaly detection models. [3] The diagnostic device according to claim 2, wherein the abnormality detection model is selected from the plurality of abnormality detection models according to elements constituting the diagnosis object, a configuration of an industrial machine in which the diagnosis object is installed, a setting status of the industrial machine, an operation program for operating the industrial machine, and an environment in which the diagnosis object is installed. [4] The diagnostic device according to claim 2 or 3, further comprising a receiving unit that accepts at least one of information specifying the type of the first state data and information specifying the type of the abnormality detection model. [5] The diagnostic device according to any one of claims 1 to 4, wherein the first acquisition unit changes a cycle for acquiring the first state data. [6] The diagnostic device according to any one of claims 1 to 5, wherein the second state data is vibration data acquired when the diagnostic object is subjected to a frequency sweep or vibration data acquired when the diagnostic object is excited. [7] The diagnostic device according to any one of claims 1 to 6, wherein the emergency diagnostic unit diagnoses a frequency component of the second state data to diagnose the cause of the abnormality. [8] The diagnostic device according to any one of claims 1 to 7, wherein the second state data includes various data, and the emergency diagnosis unit diagnoses the cause of the abnormality occurred in the diagnosis object based on a correlation between the various data. [9] The diagnostic device according to any one of claims 1 to 8, further comprising a history storage unit that records a history of cause information indicating the cause of the abnormality diagnosed by the emergency diagnosis unit, wherein the emergency diagnosis unit diagnoses the cause based on the cause information recorded in the history storage unit. [10] The diagnostic device according to any one of claims 1 to 9, wherein the normal diagnostic unit comprises a first diagnostic unit and a second diagnostic unit, where the first diagnostic unit diagnoses whether the anomaly is present in the diagnostic object, wherein the second diagnostic unit diagnoses a type of anomaly occurring in the diagnostic object.

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