Power transmission mechanism management device, power transmission mechanism management method and management system

Abstract

The present invention provides a management device capable of detecting the sensing of an abnormality (degradation) of a power transmission mechanism more precisely and accurately. A management device 30 for a power transmission mechanism that transmits a driving force from an electric motor 13 to a load-side device 12 comprises a current acquisition unit that acquires a current value of the electric motor 13 per unit process for driving the power transmission mechanism, a feature calculation unit 121 that divides the unit process into a plurality of sections and calculates an average current value by averaging the current values of the respective sections, and a diagnosis unit 122 that detects an abnormality. The diagnosis unit 122 calculates a state quantity estimation value on the basis of the average current value of the plurality of sections, and detects an abnormality in unit process on the basis of the state quantity estimation value.

Description

Technical Field

[0001] The present invention relates to a management device for a power transmission mechanism, a management method for a power transmission mechanism, and a management system.

Background Art

[0002] For example, as a device that supplies power from a power source to a load-side device via a power transmission mechanism, various industrial devices such as an injection molding machine and a press device can be cited. Taking an injection molding machine as an example, a rotating electric machine (motor) is used as a power source, and a soft viscous material such as resin, metal fiber, or a mixture thereof is injected into a mold with a predetermined mold via an arbitrary power transmission mechanism to obtain an arbitrary molded product. A device is known.

[0003] As an example, an injection molding machine will be described in terms of its configuration and operation. The injection molding machine transmits the power of an electric motor as a drive source (which may be a rotational force or a horizontal power such as a linear motor) as power to be injected into a mold for injection molding by a power transmission mechanism, and injects a soft viscous member into a predetermined mold to obtain a desired molded product. As a more specific example, a power conversion mechanism that converts the rotational driving force of an electric motor into a linear motion such as a ball screw is directly or indirectly mechanically connected, and a nut member that engages with the linear power of the ball screw, which is such a power transmission mechanism, is integrated, but the injection shaft is configured to press a soft viscous member into a predetermined mold.

[0004] Managing abnormalities in the entire load-side device with a drive source (the drive source itself, the work mechanism of the load-side device, etc.), including injection molding machines and press devices, is an important factor that deeply affects the quality of the final molded product. In addition, abnormalities cause overloads of equipment and parts, and issues in terms of the environment such as energy efficiency and issues in terms of business such as production stoppages due to equipment damage are also in the future. It can be said that managing equipment abnormalities is an issue with a great impact on society.

[0005] As a technology related to such abnormality detection, Patent Document 1 discloses a technology for estimating the state of a device. Specifically, it discloses a technology that includes a motor as a drive source and motor control means for controlling the motor, creates an internal value of the motor control of the motor control means, and estimates an abnormality of the device by comparing it with this value. It is a technology capable of detecting deterioration of a device (a load-side device and its accompanying workpiece) by monitoring the internal value of the motor control.

[0006] Furthermore, Patent Document 2 discloses an abnormality diagnosis device for a power transmission mechanism that transmits power from a motor as a drive source and an abnormality diagnosis method thereof. More specifically, Patent Document 2 obtains a current spectrum waveform from a value transmitted from a current detector connected to a motor in a configuration where the power of the motor is connected to mechanical equipment as a load via a pulley belt or a gear chain, and based on the spectrum peak calculated by analysis from this, by counting the number of sideband waves outside the frequency band of the pulley belt or gear chain that occurs with the rotational speed, it diagnoses the abnormality of the pulley belt or gear chain.

[0007] Starting from injection molding machines and press devices, since the power transmission mechanism functions as an intermediary between elements such as a power source and a mold where a load directly occurs, maintaining the performance of the power transmission mechanism deeply affects the quality (degree of completion) of the molded product that is the final product, and it is important to manage this.

Prior Art Documents

Patent Documents

[0008]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0009] Here, the detection of abnormalities (deterioration) in the power transmission mechanism will be considered. In many cases, the way the load is applied to the load-side device is not uniform, and there are often a mixture of parts where the load applied to the power transmission mechanism is large and parts where it is small. That is, as examples of the power transmission mechanism, ball screws, pulley belts, or gear chains can be cited, but depending on the load situation of the load-side device, there is a bias in the locations where deterioration occurs in these.

[0010] In this regard, the abnormality diagnosis technology of the power transmission mechanism disclosed in the above Patent Document 2 detects abnormalities in the power transmission mechanism by monitoring the spectral peaks from the current spectrum waveform and the sideband waves associated therewith. However, there remains a problem that when the deterioration of the power transmission mechanism is small or in the initial stage, the sensitivity to detect it as an abnormality decreases. That is, even if the current spectrum of the motor is simply analyzed with respect to the deterioration of the power transmission mechanism, the abnormal values caused by the deterioration of the power transmission mechanism often appear only as extremely small fluctuations. It is difficult to distinguish whether it is a temporary current noise during normal operation or a fluctuation caused by an abnormality. Just monitoring the state of the current spectrum alone leaves problems in the accuracy of abnormality detection.

[0011] Also, depending on the deterioration of the power transmission mechanism, the abnormal values may vary compared to the normal values. It is considered that it is also possible to perform a deterioration determination by judging this variation. Therefore, a technology for detecting abnormalities in the power transmission mechanism with higher accuracy and certainty is desired.

Means for Solving the Problems

[0012] In order to solve the above problems, it is configured as follows.

[0013] A management device for a power transmission mechanism that transmits the driving force from an electric motor to a load-side device, comprising: a current acquisition unit that acquires the current value of the electric motor per unit process in which the power transmission mechanism is driven; a feature amount calculation unit that divides the unit process into a plurality of sections and calculates an average current value obtained by averaging the current values for each section; calculates the difference between a reference reference current value and the average current value for each section as a feature amount, and among the feature amounts for each section, the feature amount that is the maximum average current value in the unit process, and the average current value having the greatest difference in current value from the maximum average current value as the feature amount that is the minimum average current value, the absolute value of the difference between the feature amount that is the maximum average current value and the feature amount that is the minimum average current value to state A diagnosis unit that calculates the value as an estimated state quantity and performs abnormality detection in the unit process based on the estimated state quantity value. Also, a management device for a power transmission mechanism that transmits the driving force from an electric motor to a load-side device, the management device comprising , a current acquisition unit that acquires the current value of the electric motor for each unit process in which the power transmission mechanism is driven; a feature amount calculation unit that divides the unit process into a plurality of sections and calculates an average current value obtained by averaging the current values for each section; calculates a difference between a reference current value as a reference and the average current value for each section as a feature amount, and classifies the difference into a first average current value group having a current value larger than the reference current value and a second average current value group having a current value smaller than the reference current value, and calculates an absolute value of a difference between an average value of the feature amounts of the first average current value group and an average value of the feature amounts of the second average current value group as a state quantity estimation value, and a diagnosis unit that performs abnormality detection in the unit process based on the state quantity estimation value. Also, a management device for a power transmission mechanism that transmits the driving force from an electric motor to a load-side device, the management device comprising a current acquisition unit that acquires the current value of the electric motor for each unit process in which the power transmission mechanism is driven; a feature amount calculation unit that divides the unit process into a plurality of sections and calculates an average current value obtained by averaging the current values for each section; calculates a difference between a reference current value as a reference and the average current value for each section as a feature amount, and classifies the difference into a first average current value group in which the difference between the reference current value and the average current value for each section is a positive value and a second average current value group in which the difference is a negative value, compares the number of the feature amounts of the first average current value group with the number of the feature amounts of the second average current value group, and calculates, as a state quantity estimation value, a value having the largest absolute value of the difference from the reference current value among the average current value groups having a larger number of the feature amounts, and a diagnosis unit that performs abnormality detection in the unit process based on the state quantity estimation value. .

[0014] Further, a management method for a power transmission mechanism that transmits the driving force from an electric motor to a load-side device, comprising: a current acquisition step of acquiring the current value per unit process in which the power transmission mechanism is driven; an average current value calculation step of dividing the unit process into a plurality of sections and calculating an average current value obtained by averaging the current values for each section; an abnormality detection step of performing abnormality detection, wherein the abnormality detection step calculates the difference between a reference reference current value and the average current value for each section as a feature amount, and among the feature amounts for each section, the feature amount that is the maximum average current value in the unit process, and the average current value having the greatest difference in current value from the maximum average current value as the feature amount that is the minimum average current value, the absolute value of the difference between the feature amount that is the maximum average current value and the feature amount that is the minimum average current value to state Calculate as an estimated state quantity value. [Effect of the Invention]

[0015] According to the present invention, it is possible to realize a management device, a management method, and a management system that can detect abnormalities (deterioration) of a power transmission mechanism with higher accuracy and certainty.

[0016] In particular, according to the present invention, even when the abnormality (deterioration) of the power transmission mechanism is small or in the initial stage, there is an effect of improving the accuracy and certainty that can be detected as an abnormality. Other problems, configurations, and effects of the present invention will become apparent from the following description.

Brief Description of the Drawings

[0017]

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Embodiments for Carrying Out the Invention

[0018] Hereinafter, embodiments for carrying out the present invention will be described in detail with reference to the drawings.

Examples

[0019] (Example 1) Fig. 1 schematically shows a partial schematic configuration of an injection molding machine 1 including a management device (control unit 30) for a power transmission mechanism to which the present invention is applied. In this embodiment, the injection molding machine will be described as an example, but the present invention is not limited thereto, and can be applied to any device that transmits the driving force of a driving source to a load side through a power transmission mechanism, such as a press device or a cutting device.

[0020] First, the mechanical configuration and operation of the injection molding machine 1 will be described. The injection molding machine 1 converts the rotation of a plurality of motors into linear motion to drive a single linear movement member, and at this time, the plurality of motors are operated synchronously so that their advancing positions are aligned. Note that the applicable configuration of the present invention may also be a configuration in which a single motor supplies driving force to a plurality of power transmission mechanisms via gears, or a configuration in which a single motor supplies driving force to a single power transmission mechanism.

[0021] The injection molding machine 1 can pour the molten resin into a hole 11 provided in a fixed mold 12B of a movable mold 12 and produce a resin molded product according to the shape of the gap existing between the movable mold 12A and the fixed mold 12B. The mold 12 having the movable mold 12A and the fixed mold 12B is an example of a load-side device.

[0022] The mold includes a fixed mold 12B fixed to a housing, and a movable mold 12A that moves back and forth. It includes a motor 13 which is an electric motor, a pulley 14 fixed to the output shaft of the motor 13, a driven pulley 15, a timing belt 16 that transmits the rotation of the driving pulley 14 to the driven pulley 15, a ball screw mechanism 20 as a power transmission mechanism that converts the rotation of the pulley 15 into linear motion and transmits it to the movable mold 12A, and a control unit 30.

[0023] The motor 13 is provided with an encoder (not shown) that outputs a motor position signal S2 indicating its advancing position (corresponding to the advancing position of the ball screw mechanism 20). The injection molding machine 1 is configured to drive and control the motor 13 when the control unit 30 receives a original speed command signal S0 from a higher-level device (not shown). When the motor 13 is driven, its rotation is transmitted to the screw shaft 17 of the ball screw mechanism 20 via the drive pulley 14, the timing belt 16, and the driven pulley 15. The nut portion 18 that engages via balls in these grooves converts the rotational force into linear motion. The movable mold 12A is integrated or mechanically coupled with the nut portion 18, and the movable mold 12A also moves linearly in response to the linear motion of the nut portion 18. As a result, the movable mold 12A approaches or moves away from the fixed mold 12B. After bringing the movable mold 12A into contact with the fixed mold 12B, resin is poured in for molding. After cooling and solidifying the molded product, the movable mold 12A is separated from the fixed mold 12B to take out the molded product.

[0024] The control unit 30 is configured to have, for example, a microcomputer for embedded devices including a CPU, ROM, RAM, EEPROM, various I / O interfaces, etc., and is adapted to execute various functions in cooperation with a program. The control unit 30 executes the control of the injection molding machine 1, and performs the control of the entire molding process such as, for example, the plasticization operation, the injection operation, the mold opening / closing operation, the eject operation, etc. Note that the present invention is not limited to this embodiment, and a part of it may be configured by an analog circuit.

[0025] Next, the control unit 30 will be described as a functional configuration.

[0026] FIG. 2 schematically shows a functional block diagram of the control unit 30.

[0027] The inverter 40 is controlled by a motor control unit 41 to which a so-called vector control method is applied. The motor control unit 41 acquires information such as motor current, motor voltage, rotor position information, and rotational speed from the inverter 40 or the motor 13, and creates a voltage command value for driving the motor 13 according to a command from a host controller based on these information. Then, the motor control unit 41 gives the created voltage command value to the inverter 40.

[0028] The external data acquisition unit 47 is composed of sensors installed other than the motor 13 and the inverter 40, and acquires the temperature of the equipment, the outside air temperature, the upper command value of the equipment, and the like.

[0029] The state estimation unit 42 includes a control internal value creation unit 43 that creates internal values for motor control, and a state calculation unit 44 that calculates feature quantities and state quantities related to the injection molding machine 1 based on the internal values for motor control created by the control internal value creation unit 43.

[0030] The control internal value creation unit 43 creates internal values for motor control, which are state variables in the motor control unit 41 and are related to the state of the injection molding machine 1, based on the time-series data acquired by current sensors, voltage sensors, and position sensors that are independently installed separately from those for the motor control unit 41 at the input or output part of the motor 13, and the data acquired by the external data acquisition unit 47. The control internal value creation unit 43 corresponds to the current acquisition unit.

[0031] The state calculation unit 44 has a state estimation model, and uses the state estimation model to calculate state quantities indicating the state of the equipment system, that is, the state of the equipment itself and the state of the product (such as quality) manufactured by the equipment, based on the internal values for motor control created by the motor control internal value creation unit 43. That is, the state estimation unit 42 inputs the data acquired by the above-mentioned respective sensors and the external data acquisition unit 47, creates internal values for motor control created from the input data, and outputs state quantities calculated based on the created internal values for motor control or information regarding the state of the injection molding machine 1 indicated by these state quantities (hereinafter referred to as "estimated state"). The estimated state output from the state estimation unit 42 is transmitted to an information transmission unit 45 and a motor control update unit 46, which will be described later. The information transmission unit 45 is also a display unit.

[0032] In response to the estimated state output from the state estimation unit 42, the information transmission unit 45 notifies information regarding the state of the injection molding machine 1, such as the characteristic amount of the equipment itself (deterioration determination of the screw shaft 17 described later) or information regarding the quality of the product and its changes, to the operator using the equipment system or the administrator of the equipment system by means of a display, voice, lamp, vibration, etc. As a result, the work load can be reduced in grasping the maintenance timing of the equipment, grasping the situation at the time of quality change, and equipment adjustment work, etc.

[0033] Based on the estimated state output from the state estimation unit 42, the motor control update unit 46 changes the motor control unit 41, that is, the control command, control parameter, or control software. For example, when the quality of the product has changed, the motor control update unit 46 changes the motor control unit 41 so as to suppress the change in quality. As a result, the adjustment work of the injection molding machine 1 can be automated, and the work load is reduced.

[0034] Next, the motor control unit 41 and the state estimation unit 42 will be described in more detail.

[0035] FIG. 3 is a block diagram schematically showing the functional configuration of the motor control unit 41.

[0036] In FIG. 3, the command from the upper controller is the position command θ*, but it may also be the speed (rotation speed) command ω* or the torque command Trq*. When the commands from the upper controller are the speed (rotation speed) command ω* and the torque command Trq*, the block diagrams of the motor control unit 41 are the block diagrams on the right side of the boundary line A and the block diagram on the right side of the boundary line B in FIG. 3, respectively.

[0037] As shown in FIG. 3, when the position command θ* is input from the upper controller to the motor control unit 41, the speed command creation unit 101 creates and outputs the speed command ω* based on the difference between the position feedback value θ m measured by the sensor and the position command value θ*.

[0038] When the torque command generation unit 102 receives the speed command ω*, it generates and outputs a torque command Trq* based on the difference between the speed (rotation speed) feedback value ω measured by the sensor and the speed command ω*. m

[0039] When the current command generation unit 103 receives the torque command Trq*, it generates and outputs a current command on the dq axes in the rotating coordinate system, that is, a d-axis current command Id* and a q-axis current command Iq*, based on the torque command Trq*.

[0040] When the voltage command generation unit 104 receives the d-axis current command Id* and the q-axis current command Iq*, it generates and outputs a voltage command on the dq axes, that is, a d-axis voltage command Vd* and a q-axis voltage command Vq*, based on the difference between the d-axis current feedback value Id and the d-axis current command Id*, and the difference between the q-axis current feedback value Iq and the q-axis current command Iq*.

[0041] Here, the d-axis current feedback value Id and the q-axis current feedback value Iq are obtained by performing a three-phase / two-phase conversion on the U-phase current feedback value Iu, V-phase current feedback value Iv, and W-phase current feedback value Iw of the motor measured by the sensor using the three-phase / two-phase conversion unit 106.

[0042] When the two-phase / three-phase conversion unit 105 receives the d-axis voltage command Vd* and the q-axis voltage command Vq*, it converts the d-axis voltage command Vd* and the q-axis voltage command Vq* into a U-phase voltage command Vu*, a V-phase voltage command Vv*, and a W-phase voltage command Vw*, and outputs these voltage commands to the inverter 40.

[0043] Next, the state estimator 42 will be described.

[0044] As described above (see Figure 2), the state estimator 42 includes a control internal value creation unit 46 and a state calculation unit 44. Hereinafter, each will be described with reference to the drawings.

[0045] Figure 4 schematically shows the functional configuration of the control internal value creation unit 43. ​As shown in FIG. 4, the control internal value creation unit 43 is, so to speak, the inverse model of the motor control unit 41 shown in FIG. 3. That is, the control internal value creation unit 46 corresponds to the speed command creation unit 101, torque command creation unit 102, current command creation unit 103, voltage command creation unit 104, two-phase / three-phase conversion unit 105, and three-phase / two-phase conversion unit 106 in the motor control unit 41 (see FIG. 3), and has a speed command creation unit inverse model 111, torque command creation unit inverse model 112, current command creation unit inverse model 113, voltage command creation unit inverse model 114, three-phase / two-phase conversion unit 115, and three-phase / two-phase conversion unit 116, respectively.

[0046] In FIG. 4, the command from the upper controller to the motor control unit 41 is the position command θ*, but it may also be the torque command Trq* or the speed command ω*. When the commands from the upper controller are the torque command Trq*, the speed command ω*, and the position command θ*, the block diagrams of the motor control internal value creation means 6 are the block diagrams to the right of the boundary line C, the block diagram to the right of the boundary line D, and the block diagram to the right of the boundary line E in FIG. 4, respectively.

[0047] With the configuration shown in FIG. 4, the control internal value creation unit 43 calculates the d-axis current feedback value Id, q-axis current feedback value Iq, d-axis voltage command Vd*, q-axis voltage command Vq*, d-axis current command Id*, q-axis current command Iq*, torque command Trq*, speed command ω*, and position command θ* based on any one or more of the motor three-phase voltage feedback values Vu, Vv, Vw, motor three-phase current feedback values Iu, Iv, Iw, speed feedback value ωm, and position feedback value θm, which are time-series data obtained by current sensors, voltage sensors, and position sensors that are installed independently of the motor control unit 41 at the input or output of the motor 13.

[0048] In addition, in the present embodiment, the state variables of the motor control unit 41, namely θ*, θm, ω*, ωm, Trq*, Id*, Iq*, Id, Iq, Vd*, Vq*, Vu*, Vv*, Vw*, Vu, Vv, Vw, Iu, Iv, Iw, the differences between the command values and the measured values, and the output values of the proportional, integral, and derivative controllers that make up the controller are internal values of the motor control. That is, any one or more of these motor control internal values in the motor control unit 41 are created by the control internal value creation unit 43.

[0049] In addition, in the present embodiment, the control internal value creation unit 43 shown in FIG. 4 can also create state variables (for example, Id*, Iq*, Id, Iq, Vd*, Vq*) that are created and used in the process of processing by the motor control unit 41 among the state variables of the motor control unit 41 and are not output from the motor control unit 41. As a result, the present embodiment can be applied to the estimation of various states of the various injection molding machines 1.

[0050] FIG. 5 is a block diagram schematically showing the functional configuration of the state calculation unit 44.

[0051] As described above, the state calculation unit 44 (see FIG. 2) calculates a state quantity indicating the state of the injection molding machine 1, that is, the state of the device itself and the state (such as quality) of the product manufactured by the device, based on at least one internal value of the motor control created by the control internal value creation unit 43. Note that the state calculation unit 44 may calculate the state quantity based on data (such as the temperature of the device) acquired by the external data acquisition unit 47 (see FIG. 2) in addition to the internal value of the motor control. Therefore, in FIGS. 5, 6A, and 6B, the internal values (X1 to Xn) of the motor control and the data (Z1 to Zn) acquired by the external data acquisition unit 47 are input to the state calculation unit 44.

[0052] X1 to Xn in FIG. 5 indicate the internal values of the motor control, and Z1 to Zn indicate the information acquired by the external data acquisition unit 47. At least one internal value of the motor control is input to the state calculation unit 44. Also, the presence or absence and the number of inputs of the information acquired by the external data acquisition unit 47 to the state calculation unit 44 are arbitrary.

[0053] The types and numbers of the internal values of motor control and the information acquired by the external data acquisition unit 47 input to the state calculation unit 44 are set according to the configuration of the state calculation unit 44 (for example, the statistical model described later).

[0054] In addition, in FIG. 5, for the sake of convenience, the subscripts of Xn, Zn, and Cn (described later) are made the same "n", but this "n" indicates that the numbers of Xn, Zn, and Cn are arbitrary, and does not mean that the numbers of Xn, Zn, and Cn are the same.

[0055] In the configuration example of FIG. 5, the state calculation unit 44 has a regression equation as a statistical model used for calculating the state quantity. In this configuration example, the state calculation unit 44 includes a feature quantity calculation means 121 for setting a feature quantity that becomes an explanatory variable of the regression equation, and an arithmetic unit 122 for calculating a state quantity (objective variable) by the regression equation based on the feature quantity set by the feature quantity calculation unit 121. The arithmetic unit 122 is a diagnosis unit.

[0056] The feature quantity calculation unit 121 inputs the internal value Xn and the information Zn, and calculates a feature quantity (explanatory variable) Cn to be input to the arithmetic unit 122 based on the input Xn and Zn. The feature quantity calculation unit 121 outputs the instantaneous data of Xn and Zn as the feature quantity Cn without processing, or outputs the results (such as amplitude and phase) of frequency analysis of the instantaneous data of Xn and Zn in a predetermined time interval, the effective value, average value (such as current average value), standard deviation, maximum value or minimum value, and the overshoot amount and peak value in a predetermined time interval. The number of the feature quantities Cn may be single or plural according to the regression equation.

[0057] In addition, the feature quantity calculation unit 121 may output a predetermined quantity calculated from the internal value of motor control, for example, active power, reactive power, etc. as a feature quantity. Also, disturbance torque estimated by a so-called observer may be used as a feature quantity. Note that these feature quantities may be output after further performing frequency analysis and statistical calculations (averaging), etc.

[0058] The calculation unit 122 receives the feature amounts C1 to Cn output from the feature amount calculation unit 121, and calculates the estimated state amounts (Ya, Yb) based on the feature amounts C1 to Cn.

[0059] Here, a method for calculating feature amounts related to the power transmission mechanism (particularly, the screw shaft 17 of the ball screw mechanism 20) by the injection molding machine 1, which is one of the features of the present embodiment, and a method for determining abnormalities of the device will be described.

[0060] FIG. 6A schematically shows the operation of the ball screw mechanism 20 of the injection molding machine 1 and the location of deterioration. When the ball screw mechanism 20 is used over a long period of time, the groove of the screw shaft 17 deteriorates. At this time, the groove of the screw shaft 17 may deteriorate uniformly, but it is more common for it to deteriorate sequentially from a specific location due to uneven usage frequency. For example, in the case of FIG. 6A, it shows a situation where a deterioration location Z has occurred in the nut portion 18 at a portion closer to the second half from the middle point of the screw shaft 17. Such deterioration causes instability in the mold opening and closing operation, so it is desirable to detect it accurately at an early stage.

[0061] FIG. 6B schematically shows how the current changes from the start position (start time) to the end position (end time) of the ball screw mechanism 20 and the like. First, when the nut portion 18 moves in the injection direction from the start position (start time) due to the rotation of the screw shaft 17, the current increases with the injection stress. Then, when the nut portion 18 reaches the position (elapsed time) X, the current rises in a convex shape (dotted line). This is because more friction is generated due to the deteriorated location Z of the screw shaft 17 than in the normal state, and accordingly, the motor torque for driving the ball screw mechanism 20 increases.

[0062] In this way, the feature amount calculation unit 121 can detect the deterioration of the screw shaft 17 by monitoring the change in the current value from the start position (start time) to the end position (end time).

[0063] However, for example, when the deterioration is in an initial stage or there are fluctuations such as noise in the current value, there is usually a problem that it is difficult to significantly determine the change in the current. That is because the difference from the normal current value is very small for these cases. Therefore, in this embodiment, the ball screw mechanism 20 divides the process from the start point to the end point into a plurality of predetermined regions, and calculates the average value of the current value in each region. The difference value between the average value of the current when defined as the normal state in advance and the average value of the current at the time of diagnosis is calculated for each region. Then, the maximum value of the difference values of the calculated regions is extracted as a feature amount. By comparing this feature amount with a predetermined threshold value, the presence or absence and the degree of deterioration of the screw shaft 17 are determined.

[0064] FIG. 7A and FIG. 7B schematically show the manner of deterioration determination based on the region division and the feature amount of this embodiment. In FIG. 7A, the section of one process (unit process) regarding injection from the start point to the end point is divided into an arbitrary plurality of regions. For example, in the case of the injection molding machine 1 as in this embodiment, the position where the nut portion 18 is located on the screw shaft 17 can be detected by the rotation speed of the motor 13. For example, if the rotation speed of the motor 13 in one process is 30 rotations, it is divided into three sections 1 to 3 every 10 rotations. Note that the division method is not limited to being equal and may be unequal. For example, when the deterioration part can be predicted to some extent from empirical rules or experiments in advance, the section where the deterioration is expected may be divided to be larger (or smaller) than other sections.

[0065] The feature amount calculation unit 121 and the calculation unit 122 measure the current values and the number of current values below the threshold value (normal value) among the current values detected at a predetermined time interval in each section, and calculate the average of these. Similarly, the feature amount calculation unit 121 measures the current values and the number of current values larger than the threshold value (abnormal values) among the current values detected at a predetermined time interval in each section, and calculates the average value of these. Then, the feature amount calculation unit 121 and the calculation unit 122 output these results to the state estimation unit 42.

[0066] Fig. 7B schematically shows the characteristic values (average values) of the current in each section. In this figure, it can be seen that in section 3, the average value of the abnormal values is larger than the average value of the normal values and is the largest compared to other sections. The state estimation unit 42 determines that the screw shaft 17 is deteriorated, and outputs the deterioration and the deterioration position to the motor control update unit 46 and the information transmission unit 45.

[0067] Figs. 8A and 8B schematically show an example of result comparison between the case where deterioration determination based on such characteristic values is performed and the case where it is not performed. In Figs. 8A and 8B, the horizontal axis represents the number of samples (= data for 50 processes each for normal and deteriorated cases), and the vertical axis represents the characteristic value (difference value of the average value of the current) in each sample.

[0068] Fig. 8A is an example of the case where only the current values are compared without performing the above-described deterioration determination process. That is, the difference value between the average value of the current during normal operation (the average value of the current for 50 processes of normal data in this verification) and the average value of the current in each sample is calculated as the characteristic value and shown for each cycle. As shown in this figure, the difference value D (difference amount) between the average values of the normal value and the abnormal value may be small.

[0069] On the other hand, when the above deterioration determination process is performed as shown in FIG. 8B, the difference value (difference amount) D between the normal current value group and the abnormal current value group increases as compared with the case shown in FIG. 8A, and it can be seen that the difference between normal and deterioration expands (that is, the detection sensitivity of deterioration increases). That is, in the above deterioration determination, first, one process is divided into a plurality of regions, and the average of the normal value and the abnormal value in each divided section is calculated. Therefore, the number of samples for calculating the average value is small, and the degree to which the outstanding value affects the average value is higher than the case of calculating the average value without performing the deterioration determination process (the method of FIG. 8A). Then, from among the divided regions that are easily affected by such outstanding values, the abnormal average value of the section with a high average value of abnormal values is treated as the abnormal value in the one cycle (process). As a result, the difference between the average value of the most abnormal current value and the normal average value appears as a relatively large current value difference. That is, even when the fluctuation range of the current value is small, it is possible to clearly determine normal and abnormal, and a remarkable effect can be obtained in that the accuracy of deterioration detection is improved and detection at an early stage of deterioration is also possible.

[0070] As described above, according to the present embodiment, one process is divided into a plurality of parts, the average values of the normal values and the abnormal values in each section are calculated, and the value with the highest abnormal value average among these is made the target of deterioration determination. Therefore, it is possible to detect the deterioration of the power transmission mechanism with higher accuracy and certainty. In particular, according to the present invention, even when the deterioration of the power transmission mechanism is small or in the initial stage, an improvement effect in accuracy and certainty that can be detected as abnormal can be expected.

[0071] Here, depending on the deterioration of the power transmission mechanism, the abnormal values may vary compared to the normal values, and it is considered that it is also possible to perform the deterioration determination by determining this variation. By determining the above variation, it is possible to detect the abnormality of the power transmission mechanism with higher accuracy and certainty.

[0072] FIG. 9 is a diagram for explaining the variation in the state quantity of the power transmission mechanism, that is, the variation in abnormal values. In FIG. 9, the vertical axis represents the estimated state quantity, and the horizontal axis represents the elapsed date and time. In FIG. 9, at time t0, the normal model is measured, and the state quantity of the power transmission mechanism in a normal state is set. At time t1, the load is changed from small to medium, and at time t2, the load is changed from medium to large. Then, at time t3, the load is changed from large to small, and at time t4, the machine is being repaired.

[0073] In this case, the state quantity E increases while fluctuating from time t0 to time t3, but it changes within the normal fluctuation range (variation). Regarding the period from time t3 to t4, the fluctuation range (variation) of the state quantity E is larger than that from time t0 to t3, and it can be determined that an abnormality has occurred.

[0074] FIG. 10 is a diagram for explaining the detection of peak values of characteristic quantities. In FIG. 10, for the time change of current data, characteristic quantities are derived in the diagnosis section, the peak value (positive side) and the peak value (negative side) of the characteristic quantity are detected, and the estimated state quantity (pk-pk value) is calculated. The vertical axis of the graph in FIG. 10 represents the characteristic quantity, and the horizontal axis represents the time interval number. The plurality of round marks in the graph represent the characteristic quantities of each time interval. The characteristic quantities larger than those of the normal model are shown in the upper half of the graph in the region with a positive difference, and the characteristic quantities smaller than those of the normal model are shown in the lower half of the graph in the region with a negative difference.

[0075] The difference (absolute value) between pk1 (maximum average current value), which is the characteristic quantity of the maximum value in the region with a positive difference, and pk2 (minimum average current value), which is the characteristic quantity of the minimum value in the region with a negative difference, is used as the estimated state quantity, and it can be determined whether an abnormality has occurred from this estimated state quantity.

[0076] Note that the "reference reference current value" used for calculating the characteristic quantity may be generated by the state estimation unit 42, or may be prepared in advance by the user as a profile. Also, it is possible for the user to obtain the average current value in advance and set it as the reference value.

[0077] FIG. 11 is a flowchart for calculating the difference between the peak value (positive side) and the peak value (negative side) of the feature amount performed by the arithmetic unit 122. In FIG. 11, the initial value is the value when n = 0, and the values of the feature amount max and the feature amount min are 0.

[0078] In step S1 of FIG. 11, it is determined whether the calculated feature amount is greater than the maximum feature amount. If the calculated feature amount is greater than the maximum feature amount, the process proceeds to step S2, the calculated feature amount is defined as the maximum feature amount, and the process proceeds to step S3. In step S1, if the calculated feature amount is less than or equal to the maximum feature amount, the process proceeds to step S3.

[0079] In step S3, it is determined whether the calculated feature amount is less than the minimum feature amount. If the calculated feature amount is less than the minimum feature amount, the process proceeds to step S4, the calculated feature amount is defined as the minimum feature amount, and the process proceeds to step S5. In step S3, if the calculated feature amount is greater than the minimum feature amount, the process proceeds to step S5.

[0080] In step S5, the minimum feature amount is subtracted from the maximum feature amount to obtain the state quantity estimated value.

[0081] The obtained state quantity estimated value can be compared with a predetermined normal state quantity estimated value to determine the occurrence of an abnormality. The determination of the occurrence of an abnormality is performed by the arithmetic unit 122, and the result can be displayed on the information transmission unit 45. In addition, it is also possible to display on the information transmission unit 45 the graph shown in FIG. 9 or the graph showing the relationship between the state quantity estimated value and the date and time shown in FIG. 10. It is also possible to display the average current value and the feature amount on the information transmission unit 45. In this case, all the measured data may be displayed, or instead of displaying all of them, it is also possible to display at specified day intervals. For example, even if data is measured every day, the display shows data every week. It is also possible to average the state quantity estimated values measured multiple times in a day and display them as the state quantity estimated value for that day.

[0082] The management method of the present invention is a management method of a power transmission mechanism that transmits the driving force from an electric motor to a load-side device, and includes a current acquisition step of acquiring a current value per unit process in which the power transmission mechanism is driven, an average current value calculation step of dividing the unit process into a plurality of sections and calculating an average current value obtained by averaging the current values for each section, and an abnormality detection step of detecting an abnormality. The abnormality detection step calculates a state quantity estimation value based on the average current values of the plurality of sections, and detects an abnormality in the unit process based on the state quantity estimation value.

[0083] According to Example 1 of the present invention, one process (time interval) is divided into a plurality of parts, the difference between the average value of the current values in each section and the normal value is calculated, and the maximum value among these is used as a feature quantity, and the variation (pk-pk value) of the feature quantities in a plurality of processes (a plurality of time intervals) is calculated as the state quantity estimation value, and based on the calculated state estimation value, the abnormality of the power transmission mechanism is determined.

[0084] Therefore, it is possible to realize a management device and a management method that can detect an abnormality (deterioration) of the power transmission mechanism with higher accuracy and certainty. In particular, according to the present invention, even when the abnormality (deterioration) of the power transmission mechanism is small or in the initial stage, there is an effect of improving the accuracy and certainty that can be detected as an abnormality.

[0085] (Example 2) Next, Example 2 of the present invention will be described.

[0086] Since the overall configuration of Example 2 of the present invention is the same as that of Example 1, illustration and detailed description are omitted.

[0087] In the above-described Example 1, as shown in FIG. 10, the peak value and the peak value of the feature quantity are detected, and the state quantity estimation value (pk-pk value) is calculated.

[0088] In contrast, in the second embodiment, the average value of the feature amounts in the region where the current value difference from the reference current value Io shown in the center of the vertical axis of the graph in FIG. 10 is positive is defined as the average value of the first current value group (FVave1), and the average value of the feature amounts in the region where the current value difference from the reference current value Io is negative is defined as the average value of the second current value group (FVave2). Then, the absolute value of the difference between the average value of the first current value group and the average value of the second current value group is calculated as the state quantity estimated value (abs(FVave1 - FVave2)).

[0089] Then, using the state quantity estimated value (abs(FVave1 - FVave2)), an abnormality determination is made.

[0090] FIG. 12 is a flowchart for calculating the state quantity estimated value performed by the arithmetic unit 122. In FIG. 12, the initial value is the value when n = 0, and n1 = n2 = 0, FVsigma1 = 0, and FVsigma2 = 0.

[0091] In step S10 of FIG. 12, it is determined whether the feature amount is greater than or equal to 0. If it is greater than or equal to 0, the process proceeds to step S11. In step S11, the first average current value group integration is performed (FVsigma1 ← FVsigma1 + feature amount). Then, the process proceeds to step S12, where n1 + 1 is set as n1, and the process proceeds to step S15.

[0092] In step S10, if the feature amount is less than 0, the process proceeds to step S13. In step S13, the second average current value group integration is performed (FVsigma2 ← FVsigma2 + feature amount). Then, the process proceeds to step S14, where n2 + 1 is set as n2, and the process proceeds to step S15.

[0093] In step S15, it is determined whether n is the final value. If it is not the final value, the process ends.

[0094] In step S15, if n is not the final value, proceed to step S16 to derive (calculate) the average value of the first average current value group (FVave1 ← FVsigma1 / n1). Then, proceed to step S17 to derive (calculate) the average value of the second average current value group (FVave2 ← FVsigma2 / n2). Then, proceed to step S18, set the state quantity estimated value to abs(FVave1 - FVave2), and end the process.

[0095] Also in Example 2 of the present invention, the same effects as in Example 1 can be obtained.

[0096] (Example 3) Next, Example 3 of the present invention will be described.

[0097] Since the overall configuration of Example 3 of the present invention is the same as that of Example 1, illustration and detailed description are omitted.

[0098] In Example 1, as shown in FIG. 10, it is a configuration in which the peak value (positive side) and the peak value (negative side) of the feature amount are detected, and the state quantity estimated value (pk - pk value) is calculated.

[0099] In contrast, in Example 3, as shown in FIG. 13A, the first average current group CL1 in the region where the difference from the reference current value Io is positive is set, and the average value of the feature amounts in the region where the difference from the reference current value Io is negative is set as the second average current value group CL2. Then, the number of feature amounts in the first average current value group CL1 is compared with the number of feature amounts in the second current value group average value group CL2 (shown in FIG. 13B). In the example shown in FIG. 13B, the number of feature amounts in the first average current value group CL1 is larger than the number of feature amounts in the second current value group average value group CL2. In this case, as shown in FIG. 13C, the feature amounts in the second current value group average value group CL2 are excluded from the abnormal diagnosis, and the abnormal diagnosis is performed using the feature amounts in the first average current value group CL1. That is, it is an example in which the abnormal diagnosis is performed using the data in the region with a larger number of data by a majority vote. This example can be applied to the abnormal diagnosis of a power transmission mechanism with large load fluctuations and a power transmission mechanism in a transient state.

[0100] FIG. 14 is a flowchart for calculating the estimated state quantity performed by the arithmetic unit 122. In FIG. 14, at step S20, the feature quantity for each time interval is calculated, and the process proceeds to step S21. At step S21, it is determined whether the feature quantities for all time intervals have been calculated. If not, the process ends. If they have been calculated, the process proceeds to step S22.

[0101] At step S22, the number N1 of feature quantities in the time intervals where the feature quantity is positive and the number N2 of feature quantities in the time intervals where the feature quantity is negative are calculated. Then, at step S23, it is determined whether the number N1 is greater than the number N2. If the number N1 is greater than the number N2, the process proceeds to step S24, where the value with the maximum absolute difference is calculated from the data group CL1 where the feature quantity is positive, and the process proceeds to step S26.

[0102] At step S23, if the number N1 is not greater than the number N2, the process proceeds to step S25, where the value with the maximum absolute difference is calculated from the data group CL2 where the feature quantity is negative, and the process proceeds to step S26.

[0103] At step S26, the estimated state quantity is calculated, and the process ends.

[0104] Also in the third embodiment of the present invention, in addition to obtaining the same effects as in the first embodiment, in the power transmission mechanism where the load fluctuation is often large and the abnormal diagnosis (abnormality detection) of the power transmission mechanism in the transient state, there is an effect of improving the accuracy and certainty that can be detected as an abnormality.

[0105] (Embodiment 4) Next, the fourth embodiment of the present invention will be described.

[0106] Since the overall configuration of the fourth embodiment of the present invention is the same as that of the first embodiment, the illustration and detailed description are omitted.

[0107] In Example 4, as shown in FIG. 15, a feature amount exceeding the positive threshold value (vmax) in the positive difference region and a feature amount less than the negative threshold value (vmin) in the negative difference region are excluded as outliers, and a state amount estimated value is calculated using a feature amount that is less than or equal to the positive threshold value and greater than or equal to the negative threshold value. The method for detecting an abnormality using the state amount estimated value can be the same as that in Example 1, Example 2, or Example 3.

[0108] FIG. 16 is a flowchart for calculating a state amount estimated value by excluding a feature amount as an outlier, which is performed by the arithmetic unit 122. In FIG. 16, the initial value is the value when n = 0, and the values of the feature amount max and the feature amount min are 0.

[0109] In step S30 of FIG. 16, it is determined whether the calculated feature amount is less than or equal to the positive threshold value (vmax) or greater than or equal to the negative threshold value (vmin). If the calculated feature amount is less than or equal to the positive threshold value (vmax) or greater than or equal to the negative threshold value (vmin), the process proceeds to step S31. In step S30, if the calculated feature amount is not less than or equal to the positive threshold value (vmax) or greater than or equal to the negative threshold value (vmin), the process ends.

[0110] In step S31, it is determined whether the calculated feature amount is greater than the feature amount max. If the calculated feature amount is greater than the feature amount max, the process proceeds to step S32, the calculated feature amount is defined as the maximum feature amount (feature amount max), and the process proceeds to step S33. In step S31, if the calculated feature amount is less than or equal to the feature amount max, the process proceeds to step S33.

[0111] In step S33, it is determined whether the calculated feature amount is less than the feature amount min. If the calculated feature amount is less than the feature amount min, the process proceeds to step S34, the calculated feature amount is defined as the minimum feature amount (feature amount min), and the process proceeds to step S35. In step S33, if the calculated feature amount is greater than or equal to the feature amount min, the process proceeds to step S35.

[0112] In step S35, the feature amount max is subtracted from the feature amount min to obtain a state amount estimated value.

[0113] Also in Example 4 of the present invention, the same effects as in Example 1 can be obtained. In addition, in the abnormality diagnosis (abnormality detection) of the power transmission mechanism in a power transmission mechanism where noise is often large, there is an effect of improving the accuracy and certainty that can be detected as an abnormality.

[0114] (Example 5) Next, Example 5 of the present invention will be described.

[0115] Since the overall configuration of Example 5 of the present invention is the same as that of Example 1, the illustration and detailed description are omitted.

[0116] Example 5 is an example in which an abnormality such as foreign matter mixing occurs in the power transmission mechanism, and it is possible to detect the occurrence of the abnormality and the abnormal location. Example 5 is an applicable example in addition to the abnormality detection in Examples 1 to 4.

[0117] FIG. 17 is a diagram for explaining a method of detecting (extracting) an abnormal occurrence position X when an abnormality such as foreign matter mixing occurs in the power transmission mechanism. FIG. 18 is a schematic diagram showing the functional configuration of the feature amount calculation unit and the calculation unit in Example 5, and a position acquisition unit 123 is added to the example shown in FIG. 5. The position acquisition unit 123 acquires position information per unit process in which the power transmission mechanism is driven. The position acquisition unit 123 acquires the position information of the power transmission mechanism at substantially the same timing as the current acquisition unit which is the control internal value creation unit 43. In addition, the position acquisition unit 123 outputs the position corresponding to the unit process when an abnormality of the power transmission mechanism is detected.

[0118] In FIG. 17, within the time from the start to the end of the abnormality diagnosis, the current value rises in an inclined manner as shown by the solid line during normal times, maintains a constant value after reaching a constant value, and then falls in an inclined manner.

[0119] If an abnormality such as foreign matter intrusion occurs within the time from the start to the end of the abnormality diagnosis, the current value pulsates as shown by the broken line. In this case, the feature amount also rises rapidly and then falls within a short time as shown by the broken line when an abnormality such as foreign matter intrusion occurs, similar to the current waveform during the abnormality. The rising amount of the feature amount becomes the state estimation value. And the position where an abnormality such as foreign matter intrusion occurs corresponds to the time point when the feature amount rises and falls between the start time position and the end time of the rotation of the motor 13. The position X of the motor 13 at this time is extracted, and the corresponding position, for example, the position of the screw shaft 17 can be extracted.

[0120] As shown in FIG. 18, the rotational position of the motor 13 is input to the position acquisition unit 123. The feature amount is output from the arithmetic unit 122 to the position acquisition unit 123. The position acquisition unit 123 acquires the abnormal position such as the foreign matter intrusion position of the screw shaft 17 from the rotational position of the motor 13 corresponding to the time point when the feature amount rises or falls, and transmits the information to the arithmetic unit 122. The arithmetic unit 122 outputs the abnormal occurrence position of the screw shaft 17 to the information transmission unit 45 together with the state estimation value Y. The information transmission unit 45 notifies the user of the occurrence of the abnormality and the abnormal occurrence position of the screw shaft 17 by display or the like.

[0121] FIG. 19 is a flowchart for explaining a method for detecting the abnormal occurrence position.

[0122] In step S40 of FIG. 19, it is determined whether the feature amount is greater than the abnormal threshold value (positive side). If the feature amount is greater than the abnormal threshold value (positive side), in step S41, the feature amount is set as the state estimation value p, and the position Xp of the screw shaft 17 at the time of abnormal occurrence is reported, and the process proceeds to step S42. Also, in step S40, if the feature amount is not greater than the abnormal threshold value (positive side), the process proceeds to step S42.

[0123] In step S42, it is determined whether the feature amount is less than the abnormal threshold value (negative side). If the feature amount is less than the abnormal threshold value (negative side), in step S43, the feature amount is set as the state estimation value m, the position Xm of the screw shaft 17 at the time of abnormality occurrence is reported, and the process ends. Also, in step S42, if the feature amount is not less than the abnormal threshold value (negative side), the process also ends.

[0124] Also in Example 5 of the present invention, in addition to obtaining the same effects as in Examples 1 to 4, when an abnormality such as foreign matter mixing occurs in the power transmission mechanism, it is possible to detect and report the occurrence of the abnormality and the abnormal location.

[0125] In addition, in the above-described Examples 1 to 4, each of the graphs shown in FIGS. 9, 10, and 15 can be displayed on the information transmission unit 45.

[0126] Further, the present invention can realize a management system including the above-described management device 30 and a power transmission mechanism. The power transmission mechanism in the management system can be configured to include a drive pulley 14, a driven pulley 15, a timing belt 16, a ball screw mechanism 20, and a nut 18. However, the power transmission mechanism applied to the management system of the present invention is not limited to the above example, and can also be applied to a power transmission mechanism such as a gear mechanism.

[0127] Moreover, the present invention is not limited to the above-described various configurations and functions, and it goes without saying that various changes and substitutions can be made without departing from the spirit thereof. For example, in the above-described embodiment, the injection molding machine 1 is used as an application example, but as already described, it can be applied to those that transmit the power of a motor, which is a drive source of a load-side device such as a press device or a cutting device, to a load-side device via a power transmission mechanism.

[0128] In the above-described embodiment, deterioration determination based on the feature amount is performed on the screw shaft 17 of the ball screw mechanism 20 as the power transmission mechanism, but it can also be applied to deterioration determination of the timing belt 16 or a chain instead thereof as the power transmission mechanism.

[0129] Also, in the above embodiment, the ball screw mechanism 20 is applied as the power transmission mechanism, but the present invention can also be applied to a screw mechanism composed of a screw bolt and a nut that does not use balls.

Explanation of Reference Numerals

[0130] 1... Injection molding machine, 11... Injection shaft, 12... Mold, 13... Motor, 14... Pulley, 15... Driven pulley, 16... Timing belt, 17... Screw shaft, 18... Nut portion, 20... Ball screw mechanism, 30... Control unit (management device), 40... Inverter, 41... Motor control unit, 42... State estimation unit, 43... Control internal value creation unit, 44... State calculation unit, 45... Information transmission unit, 46... Motor control update unit, 47... External data acquisition unit, 101... Speed command creation unit, 102... Torque command creation unit, 103... Current command creation unit, 104... Voltage command creation unit, 105... 2-phase / 3-phase conversion unit, 106... 3-phase / 2-phase conversion unit, 111... Inverse model of speed command creation unit, 112... Inverse model of torque command creation unit, 113... Inverse model of current command creation unit, 114... Inverse model of voltage command creation unit, 115... 3-phase / 2-phase conversion unit 115, 3-phase / 2-phase conversion unit, 121... Feature amount calculation unit, 122... Calculation unit (diagnosis unit), 123... Position acquisition unit, Io... Reference current value

Claims

1. A management device for a power transmission mechanism that transmits driving force from an electric motor to a load-side device, wherein the management device, a current acquisition unit that acquires the current value of the electric motor for each unit process in which the power transmission mechanism is driven; a feature quantity calculation unit that divides the unit process into a plurality of sections and calculates an average current value obtained by averaging the current values for each section; calculates the difference between a reference current value as a reference and the average current value for each section as a feature quantity, and among the feature quantities for each section, the feature quantity that is the maximum average current value in the unit process and the average current value having the largest difference in current value from the maximum average current value are defined as the feature quantity that is the minimum average current value, and calculates the absolute value of the difference between the feature quantity that is the maximum average current value and the feature quantity that is the minimum average current value as a state quantity estimation value, and a diagnosis unit that performs abnormality detection in the unit process based on the state quantity estimation value; A management device comprising the above.

2. A management device for a power transmission mechanism that transmits driving force from an electric motor to a load-side device, wherein the management device, a current acquisition unit that acquires the current value of the electric motor for each unit process in which the power transmission mechanism is driven; a feature quantity calculation unit that divides the unit process into a plurality of sections and calculates an average current value obtained by averaging the current values for each section; calculates the difference between a reference current value as a reference and the average current value for each section as a feature quantity, classifies it into a first average current value group having a current value larger than the reference current value and a second average current value group having a current value smaller than the reference current value, and calculates the absolute value of the difference between the average value of the feature quantities of the first average current value group and the average value of the feature quantities of the second average current value group as a state quantity estimation value, and a diagnosis unit that performs abnormality detection in the unit process based on the state quantity estimation value; A management device comprising the above.

3. A management device for a power transmission mechanism that transmits driving force from an electric motor to a load-side device, wherein the management device, a current acquisition unit that acquires the current value of the electric motor for each unit process in which the power transmission mechanism is driven; a feature quantity calculation unit that divides the unit process into a plurality of sections and calculates an average current value obtained by averaging the current values for each section; Calculating, as a feature amount, the difference between a reference current value serving as a reference and the average current value for each of the intervals, classifying the average current values into a first group of average current values for which the difference between the reference current value and the average current value for each interval is a positive value and a second group of average current values for which the difference is a negative value, comparing the number of feature amounts of the first group of average current values with the number of feature amounts of the second group of average current values, and calculating, as a state quantity estimation value, the value with the largest absolute value of the difference from the reference current value among the groups of average current values having a larger number of feature amounts, and a diagnostic unit that performs abnormality detection in the unit process based on the state quantity estimation value A management device comprising the same

4. In the management device according to claim 1 A management device in which the average current value is less than or equal to a positive threshold value and greater than or equal to a negative threshold value determined for each interval

5. In the management device according to claim 1 Further comprising a position acquisition unit that acquires a current value per unit process in which the power transmission mechanism is driven The position acquisition unit acquires position information at the same timing as the current acquisition unit, detects an abnormality of the power transmission mechanism based on the rise and fall of the current value acquired by the current acquisition unit, and outputs an abnormal position of the power transmission mechanism based on the position information

6. In the management device according to claim 1 A management device further comprising a display unit that displays the average current value

7. In the management device according to claim 1 A management device further comprising a display unit that displays a graph showing the relationship between the state quantity estimation value and the date and time

8. In the management device according to claim 7 A management device that displays the state quantity estimation value on the display unit at specified day intervals

9. In the management device according to claim 7 A management device that averages the state quantity estimation values measured a plurality of times a day and displays the averaged value as the state quantity estimation value for that day on the display unit [[ID= ​ ​ ​ ​ ​ ​ The abnormal detection step calculates, as a feature quantity, the difference between a reference current value serving as a reference and the average current value for each interval, and among the feature quantities for each interval, the feature quantity that is the maximum average current value in the unit process and the average current value having the largest difference in current value from the maximum average current value are used as the feature quantity that is the minimum average current value, and a management method for calculating the absolute value of the difference between the feature quantity that is the maximum average current value and the feature quantity that is the minimum average current value as a state quantity estimation value.

11. A management system comprising the management device according to any one of Claims 1 to 9 and the power transmission mechanism.

Images