Robot abnormality detection method and abnormality detection device

Abstract

To reliably detect the occurrence of an abnormality, such as a collision in a robot without simply comparing a torque value of a motor of each axis with a collision detection limit value being a threshold in the robot.SOLUTION: An abnormality detection device for detecting the occurrence of an abnormality in a robot 10 comprises: a threshold generation unit 53 that generates a threshold defined according to the speed of a motor 11 so that the threshold decreases as the speed of the motor 11 decreases and increases as the speed of the motor 11 increases; a filter 54 that extracts components included in a specific frequency band from the torque value of the motor 11; an intensity calculation unit 55 that calculates an intensity of the component extracted by the filter 54; and a comparison unit 56 that compares the intensity calculated by the intensity calculation unit 55 with the threshold generated by the threshold generation unit 53, and determines that an abnormality has occurred when the intensity exceeds the threshold.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method and apparatus for detecting the occurrence of an abnormality such as a collision in a robot. [Background technology]

[0002] Robots used for transporting workpieces, etc., may experience collisions due to interference with surrounding objects during operation. When a collision occurs during operation, it is necessary to immediately detect it and take action such as stopping the robot's operation immediately. To detect collisions in a robot, collision sensors attached to the arm or the hand at the end of the arm can be used. However, installing a collision sensor increases costs. Furthermore, the mass of the collision sensor can adversely affect the dynamic characteristics of the robot, and depending on the robot's structure, it may be difficult to install wiring to the collision sensor.

[0003] One method for detecting collisions without using collision sensors is to monitor the torque of the servo motors on each axis of the robot. When a collision occurs and disrupts the robot's movement, the torque of the servo motor on the corresponding axis increases rapidly. Therefore, if a torque increase is detected by comparing the torque with a threshold, a collision can be determined. This method requires an appropriate threshold, i.e., a collision detection limit value, for determining whether a collision has occurred, because torque may increase significantly depending on the content of an external command, even when the robot is operating normally in response to the command. Setting a collision detection limit value lower than the torque that can be generated during normal operation can result in false collision detection during normal operation. Generally, torque increases as the motor speed increases. Therefore, to prevent false detection, the collision detection limit value must be set to a value higher than the torque when the motor is driven at maximum speed. However, setting the collision detection limit value to a value higher than the torque required for driving at maximum speed results in inaccurate collision detection when the motor speed is low. At low speeds, the motor torque is low, and the increase in torque upon collision or interference is also small. Therefore, when a collision detection limit value corresponding to the maximum speed is used, there is a risk that a small collision or interference may not be detected, or that the collision or interference may be detected only after it has progressed to a certain extent, i.e., there is a risk of a time lag in detection.

[0004] As a technique for solving the problems caused by using a collision detection limit value corresponding to the maximum speed, Patent Document 1 discloses a technique in which a collision detection limit value corresponding to the motor speed is set so that it is smaller as the motor speed is lower and is larger as the motor speed is higher, and this collision detection limit value is compared with the motor torque value, and it is determined that a collision has occurred when the torque value exceeds the collision detection limit value. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2023-122808 Summary of the Invention [Problem to be solved by the invention]

[0006] The method described in Patent Document 1 detects collisions using a collision detection limit value that decreases as the motor speed decreases. However, because the motor torque value may exhibit abnormal behavior even during normal operation, simply comparing the torque value with the collision detection limit value may be insufficient when detecting abnormalities such as collisions in a robot. Particularly at low speeds, the torque value is likely to exceed the collision detection limit value even during normal operation. For example, if lubricating grease is applied to the reducers of each axis of a robot, the motor torque value may actually increase immediately after application. Setting a collision detection limit value according to speed and simply comparing the motor torque value with the collision detection limit value may result in false detection of abnormalities such as collisions at low speeds.

[0007] An object of the present invention is to provide a method and apparatus that can reliably detect the occurrence of an abnormality such as a collision in a robot without simply comparing the motor torque value with a threshold collision detection limit value. [Means for solving the problem]

[0008] An anomaly detection method according to one aspect of the present invention is a method for detecting the occurrence of an abnormality in a robot in which each axis is driven by a motor, and includes the steps of: determining a threshold value according to the speed of the motor so that the threshold value is smaller the lower the motor speed is and larger the higher the motor speed is; extracting components contained in a specific frequency band from the torque value of the motor; calculating the intensity of the extracted component; and determining that an abnormality has occurred when the intensity exceeds the threshold value.

[0009] An abnormality detection device according to one aspect of the present invention is an abnormality detection device that detects the occurrence of an abnormality in a robot in which each axis is driven by a motor, and includes a threshold generation means that generates a threshold value according to the motor speed so that the threshold value is smaller the lower the motor speed and larger the higher the motor speed, a filter that extracts components contained in a specific frequency band from the motor torque value, an intensity calculation means that calculates the intensity of the component extracted by the filter, and a comparison means that compares the intensity with the threshold value and determines that an abnormality has occurred when the intensity exceeds the threshold value. [Effects of the Invention]

[0010] According to the present invention, it is possible to reliably detect the occurrence of an abnormality such as a collision in a robot without simply comparing the torque value of a motor with a collision detection limit value, which is a threshold value. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a block diagram showing an example of the configuration of a robot system to which an abnormality detection method according to an embodiment is applied; [Figure 2] 1A and 1B are a front view and a plan view, respectively, showing an example of the configuration of a robot. [Figure 3] 10(a) to 10(d) are waveform diagrams illustrating the detection of the occurrence of a collision. [Figure 4] 6(a) to 6(d) are waveform diagrams illustrating detection of an abnormality in a reducer. DETAILED DESCRIPTION OF THE INVENTION

[0012] Next, an embodiment of the present invention will be described with reference to the drawings. The present invention relates to detecting abnormalities such as collisions in robots, and the following describes an embodiment of the present invention using an example of detecting an event in which a robot arm or hand collides with an obstacle around the robot. First, the principle of the abnormality detection method in this embodiment will be described.

[0013] Robots generally have a configuration in which multiple arms (also called links) are connected in series to a base, with end effectors such as hands at the tips of the arms. Therefore, when a collision occurs in the robot, the arms and other components vibrate. The vibration frequency is, for example, a natural frequency determined for each robot depending on its mechanism, or a frequency close to that frequency. When vibration occurs, it also appears at the motor position. When the robot is being controlled, the motor is controlled to cancel out the vibration, resulting in vibration in the motor torque value. The motor torque value may also increase immediately after applying grease to the reducer, but no vibration occurs in this case. Therefore, in this embodiment, the frequency component of the vibration occurring in the robot is extracted from the motor torque value, the intensity of that component is compared with a threshold, and a collision is determined to have occurred if the intensity of the vibration component exceeds the threshold. When a robot is moving at high speed, the vibration occurring when a collision occurs increases, but the noise component that is generated without a collision and superimposed on the torque value also increases. In order to ensure reliable detection of collisions while preventing false detections whether the robot is moving at a low speed or a high speed, the threshold value is set to be small when the motor speed is low and to be large when the motor speed is high.

[0014] FIG. 1 shows an example of the configuration of a robot system to which an anomaly detection method according to one embodiment is applied. The robot system includes a robot 10 used to transport a workpiece and a robot control device (robot controller) 50 that drives and controls the robot 10 based on external commands. The workpiece is, for example, a glass substrate used in the manufacture of liquid crystal display panels. The robot 10 includes a motor 11 that drives its arms, hands, etc., and an encoder 12 that is mechanically connected to the motor 11 and detects the rotational position of the motor 11. The robot 10 typically has multiple axes, each equipped with a motor 11 for each axis. However, for the sake of explanation, FIG. 1 shows only one motor 11 for one axis of the robot 10 and the encoder 12 connected to the motor 11.

[0015] The robot control device 50 includes a calculation unit 51 that calculates the trajectory of the robot 10 based on commands input from an external device and outputs position command values ​​to the motors 11 of each axis of the robot 10, and a drive unit 52 that drives the motors 11 based on the position command values ​​output from the calculation unit 51. The encoder 12 outputs a signal indicating the rotational position of the motor 11, and this signal is fed back to the drive unit 52, causing the drive unit 52 to perform servo control of the motor 11. When teaching the robot 10, a teaching pendant is connected to the robot control device 50, and commands from the teaching pendant are also input to the calculation unit 51 as external commands. The configuration including the calculation unit 51 and drive unit 52 described here is the same as the configuration of a general robot control device used to control industrial robots, and the calculation unit 51 and the portion of the drive unit 52 that performs servo control can be realized by, for example, a microprocessor that executes software.

[0016] To detect the occurrence of a collision in the robot 10, the robot control device 50 further includes a threshold generation unit 53 that outputs a threshold, a filter 54 that receives the torque value of the motor 11 and extracts only specific frequency components, an intensity calculation unit 55 that calculates the intensity of the frequency component extracted by the filter 54, and a comparison unit 56 that compares the intensity calculated by the intensity calculation unit 55 with the threshold generated by the threshold generation unit 53. The filter 54 is, for example, a band-pass filter, and passes components in a frequency band that includes the frequency of vibrations that may occur in the robot 10 due to a collision. When the robot 10 has multiple axes, the frequency of vibrations around each axis will differ if a collision occurs, so it is preferable that the pass frequency band of the filter 54 also differ for each axis.

[0017] The intensity calculation unit 55 calculates the average intensity of the frequency components extracted by the filter 54 using a time constant (averaging period) equal to or longer than the period of vibrations likely to occur due to a collision, typically using a time constant equal to or longer than the period corresponding to the lower limit of the pass frequency band of the filter 54. For example, the intensity calculation unit 55 calculates the RMS (Root Mean Square) value of the frequency components extracted by the filter 54, but a value other than the RMS value may also be calculated as the intensity. The comparison unit 56 is configured, for example, with a comparator circuit, and determines that a collision has occurred in the robot 10 when the value output by the intensity calculation unit 55 exceeds the threshold value output by the threshold generation unit 53. The determination result by the comparison unit 56 is output to the outside as a collision detection result. This collision detection result may be input to the calculation unit 51 to, for example, bring the robot 10 to an emergency stop when a collision occurs.

[0018] When controlling the movement of the robot 10 to a predetermined position, the torque of the motor 11 changes gradually even in the absence of a collision or other disturbance, and the period of the vibrations generated in the torque value due to a collision is significantly shorter than that of such slowly changing torque, so that the torque value of the motor 11 is a value in which the vibrations caused by the collision are superimposed on the original slowly changing torque. The filter 54 extracts only the frequency components of the vibration, and the intensity calculation unit 55 calculates the intensity of this frequency component of the vibration. In effect, the frequency components caused by the vibration are extracted as deviations from the original torque value, and the intensity of the extracted frequency components is then calculated and compared with a threshold value to determine whether or not a collision has occurred.

[0019] In the anomaly detection method of this embodiment, the torque value of motor 11 may be a torque command value calculated within drive unit 52 for servo control of motor 11, the current value of motor 11 itself, or a torque value actually measured for motor 11. The torque value actually measured for motor 11 may be a value obtained by performing inverse dynamics calculation or model calculation based on some actual measurement value. An example of an anomaly detection device based on the present invention is configured by threshold value generation unit 53, filter 54, intensity calculation unit 55, and comparison unit 56. When calculation unit 51 is configured by a microprocessor executing software, threshold value generation unit 53, filter 54, intensity calculation unit 55, and comparison unit 56 can also be realized by software executed on the same microprocessor.

[0020] Next, the threshold generation unit 53 will be described. As described above, in order to quickly and reliably detect the occurrence of a collision even when the robot 10 is moving at a low speed, the threshold generated by the threshold generation unit 53 is set to a low value when the speed of the motor 11 is low and a high value when the speed is high. If the robot 10 has multiple axes, a different threshold may be set for each axis. The speed of the motor 11 is obtained by differentiating the signal indicating the motor position from the encoder 12 and is constantly calculated by the drive unit 52. Therefore, the threshold generation unit 53 receives the motor speed from the drive unit 52, generates a threshold according to the motor speed, and outputs the threshold to the comparison unit 56. Alternatively, if the speed of the motor 11 is calculated by the calculation unit 51, the threshold generation unit 53 may receive the motor speed from the calculation unit 51, as indicated by the dashed line in the figure. In practice, it is preferable to divide the range of motor speed from zero to the maximum speed of the motor 11 into multiple speed ranges and set a single threshold value for each divided speed range. As an example, the speed of the motor 11 can be divided into three ranges: low speed, medium speed, and high speed, and a threshold value can be set for each range. Of course, the motor speed can also be divided into four or more ranges. Setting a threshold value for each speed range in this manner allows the threshold generator 53 to be implemented using a lookup table, simplifying the configuration of the threshold generator 53 and reducing the amount of calculation required to calculate the threshold value. The threshold value for each speed range can be calculated, for example, by driving the motor 11 at different speeds under conditions where a collision will not occur, measuring the torque waveform at that time, and multiplying the measured maximum torque by an appropriate safety factor. Furthermore, if torque value data from an actual collision is available, the threshold value can be set so that such a collision can be detected. The threshold values ​​for each motor speed thus obtained in advance are stored in the threshold generator 53, which is configured, for example, as a lookup table.

[0021] The abnormality detection method of this embodiment has been described above using an example of detecting a collision in the robot 10. However, when an abnormality other than a collision occurs, the arm of the robot 10 or the like may vibrate, which may result in vibrations in the torque value of the motor 11. The abnormality detection method of this embodiment can also be used to detect abnormalities other than collisions. An example of such an abnormality is an abnormality in the reducer connected to the motor 11 of each axis. When an abnormality occurs in the reducer, the reducer vibrates, and the vibration is transmitted to the robot 10. To offset this vibration, the torque value of the motor 11 also vibrates. Therefore, similar to the case of detecting a collision, an abnormality in the reducer can also be detected. Vibrations caused by the reducer often have a period corresponding to one rotation of the input or output shaft of the reducer. Therefore, the frequency of this vibration is considered to be different from the frequency of vibrations caused by a collision of the robot 10. Therefore, by setting the pass frequency band of the filter 54 to correspond to vibrations caused by an abnormality in the reducer, it is possible to detect only an abnormality in the reducer and not a collision in the robot 10, and by switching the pass frequency band of the filter 54, it is possible to distinguish between a collision in the robot 10 and an abnormality in the reducer and detect it separately.

[0022] FIG. 2 shows an example of the configuration of a robot 10 targeted by the anomaly detection method of this embodiment, with (a) being a front view and (b) being a plan view. The illustrated robot 10 is a horizontal articulated robot suitable for transporting large, plate-shaped workpieces such as glass substrates for manufacturing liquid crystal display panels. The robot 10 includes a base 21, a bent arm 22 connected to the base 21, two distal arms 23 and 24 connected to the bent arm 22, and two hands 25 and 26 connected to the distal arms 23 and 24, respectively. The bent arm 22 can be raised and lowered in the height direction as indicated by the arrow in the figure by an elevating mechanism 27 provided within the base 21, and can be rotated within a horizontal plane by being driven by a motor provided within the elevating mechanism 27. The bent arm 22 is an arm bent into an L-shape (i.e., a boomerang shape). It is connected to the other end of the base 21 at the bent position and can be rotated within a horizontal plane by being driven by a motor (not shown) built into the elevating mechanism 27. The axis of this rotation of the bending arm 22 is defined as the TH axis. The L-shaped bending arm 22 has two ends, one of which is connected to the tip arm 23, and the other of which is connected to the tip arm 24.

[0023] One distal arm 23 is connected to the bending arm 22 at its base end and is driven by a motor (not shown) built into the bending arm 22, allowing it to rotate within a horizontal plane. The axis of this rotation of the distal arm 23 is referred to as the RR axis. One hand 25 is connected to the tip of one distal arm 23 and is driven by a motor (not shown) built into the distal arm 23, allowing it to rotate within a horizontal plane. The axis of this rotation of the hand 25 is referred to as the AR axis. Similarly, the other distal arm 24 is connected to the bending arm 22 at its base end and is driven by a motor (not shown) built into the bending arm 22, allowing it to rotate within a horizontal plane, the axis of this rotation being referred to as the RL axis. The other hand 26 is connected to the tip of the other distal arm 24 and is driven by a motor (not shown) built into the distal arm 24, allowing it to rotate within a horizontal plane, the axis of this rotation being referred to as the AL axis. In order to prevent interference between the tip arms 23, 24 and between the hands 25, 26, the rotation planes formed by the rotation of each of the tip arms 23, 24 and the hands 25, 26 are arranged in the following order from the bottom in the height direction: the rotation plane of one tip arm 23, the rotation plane of one hand 25, the rotation plane of the other tip arm 24, and the rotation plane of the other hand 26.

[0024] The hand 25 is composed of a base 25A supported by the distal arm 23, multiple forks 25B extending parallel to one another in one direction from the base 25A, and a distal fork 25C attached to the tip of each fork 25B. The proximal side of the distal fork 25C is clamped by a bracket attached to the distal end of the fork 25B. When a force is applied to push the distal fork 25C toward the fork 25B during movement of the robot 10, friction between the bracket and the distal fork 25C increases the torque of the motor 11. Alternatively, the hand 25 may be configured such that the proximal side of the distal fork 25C is inserted into the fork 25B, and the distal fork 25C is pushed into the fork 25B when an external force is applied, and then protrudes from the fork 25B to its original position when the force is removed. Similarly, the hand 26 is composed of a base 26A, a fork 26B, and a distal fork 26C. In this robot 10, the motors of each axis are controlled so that the TH axis exists on the center line extending in the longitudinal direction of each of the hands 25, 26 or on an extension of that center line, and so that when one of the hands 25, 26 extends away from its position on the base 21 in the horizontal plane, the other remains on the base 21.

[0025] 3(a) to 3(d) are waveform diagrams illustrating the detection of collision occurrence in the robot 10 shown in FIGS. 2(a) and 2(b). In the diagrams, the vertical axis represents torque values ​​expressed as numerical values ​​in the unit system internal to the robot control device 50, with negative values ​​representing rotation in the reverse direction. The RMS value has the same units as the torque value. The diagrams show how the torque value of the motor 11 of the RR axis changes when the hand 25 of the robot 10 collides with a surrounding object. FIG. 3(a) shows the time change in the torque value of the motor 11 when no collision occurs, and FIG. 3(b) shows the time change in the RMS value (i.e., the output of the intensity calculation unit 55 in the robot control device 50 shown in FIG. 1) calculated for the frequency component of the collision extracted by filtering processing from the torque value when no collision occurs. Point A in FIG. 3(b) indicates the point where the RMS value after filtering is maximum under normal conditions, and the threshold generated by the threshold generation unit 53 must be greater than the RMS value at point A.

[0026] FIG. 3(c) shows the change over time in the torque value of motor 11 when a collision occurs. FIG. 3(d) shows the change over time in the RMS value calculated for the frequency components of the collision extracted from the torque value of FIG. 3(c) by filtering. Approximately 0.6 seconds after the start, tip fork 25C collides with an obstacle around robot 10, and the rotation of tip arm 23 of robot 10 begins to encounter resistance. As a result, the torque value increases as indicated by point B in the figure, and the filtered RMS value also increases as indicated by point C in the figure. Then, approximately 0.8 seconds after the start, fork 25B also collides with an obstacle, causing the torque value to rise sharply, activating a safety device separate from the device for the anomaly detection method of this embodiment, triggering an error and stopping the operation of motor 11. The threshold value generated by threshold generator 53 must be smaller than the RMS value at point C.

[0027] 4(a) to 4(d) are waveform diagrams illustrating the detection of an abnormality in the reducer of the robot 10 shown in FIGS. 2(a) and 2(b). The diagrams show how the torque value of the motor 11 of the TH axis changes when an abnormality occurs in the reducer provided on the TH axis of the robot 10. The vertical axis in the diagrams is set in the same manner as in FIGS. 3(a) to 3(d). FIG. 4(a) shows the time change in the torque value of the motor 11 when there is no abnormality in the reducer, and FIG. 4(b) shows the time change in the RMS value calculated for the extracted frequency components extracted by filtering the torque value of FIG. 4(a). Point D in FIG. 4(b) indicates the point where the RMS value after filtering is maximum under normal conditions, and the threshold value generated by the threshold generator 53 must be greater than the RMS value at point D.

[0028] FIG. 4(c) shows the time change in the torque value of the motor 11 when an abnormality occurs in the reducer, and FIG. 4(d) shows the time change in the RMS value calculated for the frequency components extracted by filtering the torque value in FIG. 4(c) to extract frequency components related to the abnormality in the reducer. When an abnormality occurs in the reducer, short-period vibration components are superimposed on the torque value. When filtering is performed to extract the frequency components of these vibrations and the RMS value is calculated, the result is a large value, as shown in FIG. 4(d). In FIG. 4(d), large peaks are observed in the RMS value, and the RMS value is maximized throughout at the central peak, as shown at point E. The threshold value generated by the threshold generator 53 must be smaller than the RMS value at point E, and is preferably smaller than the maximum RMS values ​​at the peaks on both sides of the figure.

[0029] In the case of a robot used for transporting workpieces, collisions are likely to occur when the robot starts moving or just before stopping, i.e., when the robot is moving at a low speed. The anomaly detection method of this embodiment extracts, from the torque value, the frequency components of vibrations that are likely to occur when an abnormality occurs, and then calculates their intensity, for example, their RMS value. By comparing the calculated intensity with a threshold value corresponding to the speed, it is possible to reliably detect collisions that occur at low speeds while suppressing false detections. In other words, the anomaly detection method of this embodiment described above can reliably detect the occurrence of an abnormality, such as a collision in a robot, without simply comparing the motor torque value with a threshold value, i.e., a collision detection limit value.

[0030] An example of a configuration for implementing the present invention has been described above, but the above technology can also be configured as follows.

[0031] (1) An abnormality detection method for detecting an abnormality in a robot in which each axis is driven by a motor, comprising: determining a threshold value according to the speed of the motor so that the threshold value is smaller as the speed of the motor is lower and is larger as the speed of the motor is higher; An anomaly detection method comprising: extracting a component included in a specific frequency band from the torque value of the motor; calculating the intensity of the extracted component; and determining that the anomaly has occurred when the intensity exceeds the threshold value.

[0032] (2) The abnormality detection method according to (1), wherein the range from zero speed to the maximum speed of the motor is divided into a plurality of speed regions, and the threshold is defined as a single value for each of the speed regions.

[0033] (3) The abnormality detection method according to (1) or (2), wherein the specific frequency band is a frequency band that includes the frequency of vibrations generated in the robot due to a collision.

[0034] (4) The abnormality detection method according to (1) or (2), wherein the specific frequency band is a frequency band that includes a frequency of vibrations that occur in the robot due to an abnormality in a reducer included in the robot.

[0035] (5) The anomaly detection method according to any one of (1) to (4), wherein the intensity is a root mean square value of the specific frequency component.

[0036] (6) An abnormality detection method according to any one of (1) to (5), in which the robot has a plurality of axes, the specific frequency component and the threshold value are defined for each of the axes, and whether the abnormality has occurred is determined based on the torque value of the axis.

[0037] (7) An abnormality detection device for detecting the occurrence of an abnormality in a robot in which each axis is driven by a motor, a threshold value generating unit that generates a threshold value according to the speed of the motor so that the threshold value is smaller as the speed of the motor is lower and is larger as the speed of the motor is higher; a filter that extracts components included in a specific frequency band from the torque value of the motor; an intensity calculation unit that calculates the intensity of the component extracted by the filter; a comparison unit that compares the intensity with the threshold value and determines that the abnormality has occurred when the intensity exceeds the threshold value; An abnormality detection device having the above configuration.

[0038] (8) A range from zero speed to a maximum speed of the motor is divided into a plurality of speed regions, and the threshold is set as a single value for each of the speed regions; The abnormality detection device according to (7), wherein the threshold value generation unit includes a lookup table that stores the threshold value for each of the speed ranges.

[0039] (9) The abnormality detection device according to (7) or (8), wherein the specific frequency band is a frequency band that includes a frequency of vibrations generated in the robot due to a collision.

[0040] (10) The abnormality detection device according to (7) or (8), wherein the specific frequency band is a frequency band that includes a frequency of vibrations that occur in the robot due to an abnormality in a reducer included in the robot.

[0041] (11) The anomaly detection device according to any one of (7) to (10), wherein the intensity is a root mean square value of the specific frequency component.

[0042] (12) An abnormality detection device according to any one of (7) to (11), wherein the robot has a plurality of axes, and the specific frequency component and the threshold value are defined for each of the axes, and whether the abnormality has occurred is determined based on the torque value of the axis.

[0043] According to the configurations shown in (1) and (7), the frequency components of vibrations that are likely to occur when an abnormality occurs are extracted from the torque value, and their intensity is calculated. The calculated intensity is then compared with a threshold value according to the speed. This makes it possible to reliably detect abnormalities that occur not only at high speeds but also at low speeds while suppressing the occurrence of false detections.

[0044] According to the configurations (2) and (8), it is possible to generate a threshold value using a lookup table, thereby reducing the calculation load for calculating the threshold value.

[0045] According to the configurations (3) and (9), frequency components related to vibrations generated in the robot due to vibrations are extracted, so that the occurrence of a collision in the robot can be reliably detected. Similarly, according to the configurations (4) and (10), frequency components related to vibrations generated in the robot due to a speed reducer abnormality are extracted, so that the occurrence of a speed reducer abnormality in the robot can be reliably detected. Furthermore, depending on whether a frequency band corresponding to vibrations generated by a collision or a frequency band corresponding to vibrations generated by a speed reducer abnormality is selected, it is possible to distinguish whether the abnormality occurring in the robot is a collision or a speed reducer abnormality.

[0046] According to the configurations shown in (5) and (11), by adopting the RMS (root mean square) value as the vibration intensity of a specific frequency band in the torque value, it becomes easier to calculate the intensity and it is possible to suppress the influence of noise generated in the filter used to extract the frequency components.

[0047] According to the configurations shown in (6) and (12), abnormalities can be detected for each axis that makes up the robot, and it is also possible to estimate the location where an abnormality occurred, such as estimating the location of a collision in which hand or arm of the robot. [Explanation of symbols]

[0048] 10...Robot; 11...Motor; 12...Encoder; 21...Base; 22...Bending arm; 23, 24...Tip arm; 25, 26...Hand; 25A, 26A...Base; 25B, 26B...Fork; 25C, 26C...Tip fork; 27...Lifting mechanism; 50...Robot control device: 51...Calculation unit; 52...Drive unit; 53...Threshold generation unit; 54...Filter; 55...Intensity calculation unit; 56...Comparator.

Claims

1. An abnormality detection method for detecting an occurrence of an abnormality in a robot in which each axis is driven by a motor, comprising: a threshold value that is determined according to the speed of the motor so that the threshold value is smaller as the speed of the motor is lower and is larger as the speed of the motor is higher; An anomaly detection method comprising: extracting a component included in a specific frequency band from the torque value of the motor; calculating the intensity of the extracted component; and determining that the anomaly has occurred when the intensity exceeds the threshold value.

2. 2. The abnormality detection method according to claim 1, wherein a range from zero speed to a maximum speed of the motor is divided into a plurality of speed regions, and the threshold is defined as a single value for each of the speed regions.

3. 3. The abnormality detection method according to claim 1, wherein the specific frequency band is a frequency band that includes a frequency of vibrations generated in the robot due to a collision.

4. The abnormality detection method according to claim 1 or 2, wherein the specific frequency band is a frequency band that includes a frequency of vibrations that occur in the robot due to an abnormality in a reducer included in the robot.

5. The anomaly detection method according to claim 1 , wherein the intensity is a root mean square value of the specific frequency component.

6. 3. The abnormality detection method according to claim 1, wherein the robot has a plurality of axes, and the specific frequency component and the threshold value are defined for each of the plurality of axes, and whether the abnormality has occurred is determined based on a torque value of the axis.

7. An abnormality detection device for detecting an abnormality in a robot in which each axis is driven by a motor, a threshold value generating unit that generates a threshold value that is defined according to the speed of the motor so that the threshold value decreases as the speed of the motor decreases and increases as the speed of the motor increases; a filter that extracts components included in a specific frequency band from the torque value of the motor; an intensity calculation unit that calculates the intensity of the component extracted by the filter; a comparison unit that compares the intensity with the threshold value and determines that the abnormality has occurred when the intensity exceeds the threshold value; An abnormality detection device having the above configuration.

8. a range from zero speed to a maximum speed of the motor is divided into a plurality of speed regions, and the threshold is set as a single value for each of the speed regions; The abnormality detection device according to claim 7 , wherein the threshold value generating unit includes a lookup table that stores the threshold value for each of the speed ranges.

9. 9. The abnormality detection device according to claim 7, wherein the specific frequency band is a frequency band that includes a frequency of vibrations generated in the robot due to a collision.

10. 9. The abnormality detection device according to claim 7, wherein the specific frequency band is a frequency band that includes a frequency of vibrations that occur in the robot due to an abnormality in a reducer included in the robot.

11. The anomaly detection device according to claim 7 or 8, wherein the intensity is a root mean square value of the specific frequency component.

12. 9. The abnormality detection device according to claim 7, wherein the robot includes a plurality of axes, and the specific frequency component and the threshold value are defined for each of the plurality of axes, and whether or not the abnormality has occurred is determined based on a torque value of the axis.

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