TW202040918A - Monitoring device and monitoring method - Google Patents

Abstract

This monitoring device is provided with: a current sensor for detecting two-phase current information for each of a plurality of drive motors; a motor information calculation unit for calculating the torque current or rotational speed of the corresponding drive motor from the current information; a feature quantity calculation unit for calculating feature quantities for the torque currents or rotational speeds of the plurality of drive motors; a state estimation unit for estimating a device state on the basis of the feature quantity of a drive motor having a correlation from among the plurality of drive motors; a data storage unit for storing reference value data; and an abnormality determination unit for determining an abnormality state on the basis of the estimated device state and the reference value data.

Description

Monitoring device, monitoring method, servo amplifier, industrial controller, and machine tool

The present invention relates to a monitoring technology, especially to a device for monitoring the state of machine tools and the like.

Machine tools such as comprehensive processing machines have the function of automatic tool replacement, which can be combined with the purpose to implement numerical control of milling, boring, drilling, tapping and other heterogeneous machining in one machine. In addition, the machine tool stores a plurality of cutting tools in the tool magazine and can automatically perform processing according to the instructions of the computer numerical control, so it is used for processing various parts.

However, the tools used in the machine tool have the situation that the tool tip wears and the cutting resistance increases as the processing time passes, which eventually leads to damage.

Patent Document 1 discloses the following technology, that is, to estimate and calculate the disturbance torque ys and yz acting on the spindle motor or the Z-axis (feed axis) motor and record them in the data table, and then, compare ys and yz Obtain the estimated appearance value and the threshold of movement variation, compare them, and determine the abnormality of the tool. [Prior Technical Literature] [Patent Literature]

[Patent Document 1] Japanese Patent Laid-Open No. 2003-326438

[The problem to be solved by the invention]

Paragraph No. 0033 of Patent Document 1 describes "The disturbance torque ys acting on the spindle motor is estimated based on the speed signal of the spindle motor and the torque command value to the spindle motor" and "The speed signal based on the feed axis motor The torque command value of the feed axis motor is used to estimate the disturbance torque yz acting on the feed axis motor.

That is, determining the abnormality of the tool requires motor control information such as the speed signal or torque command value of the driving motor. Therefore, in Patent Document 1, since certain components must be used to provide motor control information, in the case of existing devices, there is a problem of additional wiring and other operations. Therefore, a technology for monitoring based on the current information of the motor is required to avoid additional wiring, etc., and to be able to use existing devices.

Moreover, in the machine tool, machining is performed with the same configuration (drive motor) from a large-diameter tool to a small-diameter tool. In the case of processing large-diameter tools, the processing load is larger, and the current driving the motor is also larger. On the other hand, in the case of processing small-diameter tools, the processing load is small, and the current driving the motor is also small.

As shown in Patent Document 1, if the disturbance torque is estimated for each feed axis motor and other motors, especially in the case of machining small diameter tools, the current of the spindle motor (rotating axis motor) driving the spindle is determined by The motor current change caused by processing becomes smaller. Therefore, it is also submerged in the current during idling (no processing), and it is difficult to measure with better sensitivity.

The purpose of the present invention is to provide a monitoring device and a monitoring method for judging the abnormality of the machine state with better sensitivity based on the current information of a plurality of driving motors. [Technical means to solve the problem]

A preferred example of the present invention is a monitoring device, which includes: A current sensor, which detects two-phase current information for each of a plurality of driving motors; A motor information calculation unit, which calculates the corresponding torque current or rotation speed of the drive motor according to the current information; A feature quantity calculation unit that computes feature quantities related to the torque current or the rotation speed in a plurality of the drive motors; A state estimating unit, which estimates the machine state based on the above-mentioned characteristic quantities of the above-mentioned driving motors in a correlation relationship among a plurality of the above-mentioned driving motors; Data storage unit, which records the reference value data; and The abnormality determination unit determines the abnormal state based on the estimated state of the machine and the reference value data.

Another preferred example of the present invention is a monitoring method that detects two-phase current information for each of a plurality of driving motors, According to the above current information, calculate the corresponding torque current or rotation speed of the above drive motor, Calculate the characteristic quantities related to the above torque current or the above rotation speed in a plurality of drive motors, Based on the above-mentioned characteristic quantities of the above-mentioned driving motors in correlation among the plurality of the above-mentioned driving motors, the machine state is estimated, Determine the abnormal state based on the pre-recorded reference value data and the estimated machine state. [Effects of Invention]

According to the present invention, it is possible to better judge the abnormality of the machine state based on the current information of a plurality of driving motors.

Hereinafter, the embodiment will be described in accordance with the drawings. Furthermore, in the drawings corresponding to the respective embodiments, the same components are denoted by the same numerals. [Example 1]

1 is a block diagram of a dual-axis motor control system 101 for driving tools such as machine tools in the first embodiment. The dual-axis motor here assumes that there is a correlation between the spindle motor and the feed axis motor (Z-axis motor) during the drilling process.

In FIG. 1, the motor control system 101 includes the motor 1 10-1, the motor 2 10-2, the driving device 20, the monitoring device 40, and the current sensor 41. The drive device 20 includes an inverter 22, a current sensor 24 and a control unit 30.

The rotating shaft 14 of the motor 10-1 is directly connected to the tool 16 via mechanical parts (not shown) such as gears or ball screws, or directly connected. In addition, the motor 2 also drives the tool 16 (not shown) directly or via a mechanism. Each inverter 22 applies a three-phase AC voltage to 10-1 of motor 1 or 10-2 of motor 2 based on the control of each control unit 30.

The control unit 30 includes a CPU (Central Processing Unit), a DSP (Digital Signal Processor), a RAM (Random Access Memory), and a ROM (Read Only Memory). ) Is used as the hardware of ordinary computers, and the ROM stores the control program executed by the CPU, the micro program executed by the DSP, and various data.

In FIG. 1, the inside of the control unit 30 represents the functions implemented by the control program and the micro program in blocks. That is, the control unit 30 includes a command generation unit 32, a deviation calculation unit 33, a vector control unit 34, a dq/3Φ conversion unit 36, and a 3Φ/dq conversion unit 38.

The control unit 30 performs vector control of the motor 10 with these structures, thereby improving the responsiveness of the motor 10-1.

The inverter 22 outputs U-phase, V-phase, and W-phase AC currents to the motor 10-1. The current sensor 24 detects the current of two phases. That is, in the example shown in the figure, U-phase and W-phase currents are detected, and the results are output as current detection values I _us and I _ws .

Here, suppose that the rotation coordinate rotates at the frequency f, and the orthogonal axes in the rotation coordinate are called the d-axis and the q-axis, and the current supplied to the motor 10-1 is expressed as the direct current in the rotation coordinate.

The current in the q-axis determines the current component of the torque of the motor 10, and is referred to as the torque current hereinafter. In addition, the current in the d-axis becomes a component of the exciting current of the motor 10-1, and is referred to as the exciting current hereinafter.

The 3Φ/dq conversion unit 38 outputs the excitation current detection value I _d and the torque current detection value I _q based on the current detection values I _us and I _ws . The command generation unit 32 receives a torque command value τ* from a higher-level device not shown, and generates an excitation current command value I _d * and a torque current command value I _q * based on the torque command value τ*.

Deviation calculation unit 33 based on the current command value I _d *, I _q * and the detected current value I _d, I _q, output deviation _{I d * -I d, I q} * -I q. The vector control unit 34 outputs the excitation voltage command value V _d * and the torque voltage command value V _q * based on the deviations I _d *-I _d , I _q *-I _q and the like.

The operation of the vector control unit 34 will be described in more detail. The vector control unit 34 performs proportional integral control on the deviations I _d *-I _d and I _q *-I _q to obtain the command value of the same speed, that is, the frequency command ω1 ( Not shown).

Furthermore, the vector control unit 34 obtains the phase command θ1 (not shown) by integrating the frequency command ω1. Furthermore, the vector control unit 34 multiplies the vector composed of the current command values I _d * and I _q * by the vector of the impedance of the motor 10-1, and as a result, calculates the voltage command values V _d * and V _q *.

The dq/3Φ conversion unit 36 outputs a PWM (Pulse Width Modulation) signal for driving the inverter 22 based on the voltage command values V _d * and V _q * of the rotary coordinate system. The inverter 22 switches the supplied DC voltage (not shown) based on the supplied PWM signal, and outputs U-phase, V-phase, and W-phase voltages to the motor 10-1.

<Configuration of Monitoring Device 40> FIG. 2 is a block diagram of the monitoring device 40. The monitoring device 40 is equipped with CPU, DSP, RAM, ROM, etc. as the hardware of an ordinary computer similarly to the above-mentioned control unit 30, and the ROM stores control programs executed by the CPU, micro programs executed by the DSP, various data, etc.

In FIG. 2, the inside of the monitoring device 40 represents the functions implemented by the control program and the micro program in blocks. That is, the monitoring device 40 includes a motor information calculation unit 42, a feature quantity calculation unit 44, a state estimation unit 45, a data storage unit 46, and an abnormality determination unit 47.

The motor information computing unit 42 obtains the U-phase current detection value I _u1 of 10-1 of the motor 1, the W-phase current detection value I _w1 , and the U-phase 10-2 of the motor 2 from the corresponding current sensors 41. The current detection value I _u2 and the W-phase current detection value I _w2 .

Then, the motor information calculation unit 42 outputs the respective motor torque currents (real part currents) _Ir1 and _Ir2 and the mechanical frequencies ω _rs1 and ω _rs2 (rotation speed) based on the detected values.

Here, referring to FIG. 3, the meaning of the signals output from the motor information computing unit 42 will be described. FIG. 3 is a block diagram of the motor information computing unit 42.

The motor information computing unit 42 includes a 3Φ/αβ converter 52, an arctangent converter 54 (phase detection unit), a subtractor 56 (PLL (Phase Locked Loop) computing unit), and a phase computing unit 60 (PLL computing unit) , The rotation speed calculation unit, the rotation speed calculation process), the rotation coordinate converter 70, the integrator 72 (PLL calculation unit), and the multiplier 74. Furthermore, the phase calculator 60 includes multipliers 62 and 64, an integrator 66, and an adder 68.

The 3Φ/αβ converter 52 converts the current detection values I _u and I _w into orthogonal two-phase alternating currents I _α and I _β . The arctangent converter 54 calculates the AC current phase angle detection value θ _i * based on the AC currents I _α and I _β .

The subtractor 56 subtracts the AC current phase angle detection value θ _i * from the AC current phase angle θ _i (described in detail below). In the phase calculator 60, the multiplier 62 multiplies the difference "θ _i *-θ _i "by a specific proportional gain KpPLL.

The result of the multiplication in the multiplier 62 becomes the aforementioned proportional signal PLL_P. In addition, the multiplier 64 multiplies the difference "θ _i *-θ _i "by the specific integral gain KiPLL, and the integrator 66 integrates the result of the multiplication.

The integration result in the integrator 66 is called the integration signal PLL_I. The adder 68 adds the proportional signal PLL_P and the integral signal PLL_I, and outputs the addition result as the frequency signal ω _1s .

The integrator 72 integrates the frequency signal ω _1s , and outputs the alternating current phase angle θ _i . The alternating current phase angle θ _{i is} supplied to the subtractor 56 and also to the rotation coordinate converter 70.

Furthermore, the multiplier 74 multiplies the frequency signal ω _1s by "2/P" (here, P is the number of poles of the motor 10), and outputs the multiplication result as the mechanical frequency ω _rs . Here, the mechanical frequency ω _rs becomes a signal corresponding to the actual speed of the motor 10 (refer to FIG. 1) (including the speed of slipping in the case of an induction motor).

The rotary coordinate converter 70 converts the two-phase alternating currents I _α and I _β into the two-axis direct currents I _r and I _{i in} the rotating coordinate system rotating with the frequency signal ω _1s .

In this way, the subtractor 56, the phase calculator 60, and the integrator 72 function as a PLL (Phase Locked Loop) arithmetic unit, and outputs such as the difference "θ _i *－θ _i "output by the subtractor 56 close to "0". Class frequency signal ω _1s and alternating current phase angle θ _i .

The feature quantity calculation unit 44 extracts the maximum value, average value, FFT (fast Fourier transform) of the motor torque current _Ir , and the maximum value, average value, FFT, etc. of the motor rotation speed ω _rs .

The state estimating unit 45 estimates the state of the machine based on characteristic quantities such as the maximum value and average value of the motor torque current _Ir, the maximum value, average value, and FFT of the FFT and the motor rotation speed ω _rs .

The estimated state quantity is compared with the reference value data recorded in the data storage unit 46, and the abnormality determination unit 47 detects whether the state quantity is abnormal. In addition, the abnormality determination unit 47 outputs various alarm signals to the outside.

Furthermore, the alarm signal may be a method capable of notifying the manager, such as lighting of a lamp, buzzing of an alarm, or radio wave transmission of a wireless communication method.

When the monitoring device 40 in this embodiment is installed in a harsh environment, it is preferably stored in the housing of the monitoring device that has implemented dust and waterproof measures. Furthermore, when the monitoring device 40 is installed near a noise-generating device such as the inverter 22, it is preferable to implement noise countermeasures on the monitoring device 40.

As described above, the monitoring device 40 can use its own coordinates, and use a simple algorithm to convert AC to DC based on the current information flowing into the driving motor that has a correlation. Therefore, edge processing for judging abnormalities is also possible. Can be executed in the monitoring device. As a result, as a result, the amount of data can be drastically reduced, and analysis/diagnosis operations become easier.

＜Estimation of tool wear degree＞ Fig. 4 shows an example of a tool drive shaft of a machine tool such as an integrated processing machine. The types of integrated processing machines are roughly divided into horizontal and vertical according to the direction of the spindle (rotating shaft). The horizontal spindle is installed in the horizontal direction, and the vertical spindle is installed in the vertical direction.

The main shaft is the most important shaft in the machine tool that installs and rotates the processed product or tool to be processed. The basic structure of the vertical integrated processing machine generally performs 3-axis processing.

The action is the same as that of a bed-type milling machine in which the spindle moves in the up and down direction (Z axis) when the machine is viewed from the front, and the worktable with the processed product moves forward and backward (Y axis) and left and right (X axis).

The feed axis changes depending on the type of processing. For example, in the case of drilling, the Z axis becomes the feed axis. In the case of milling, not only the Z axis but also the X or Y axis becomes the feed axis. On the other hand, a 5-axis vertical machining center can use an indexing table with rotation (C-axis) and tilt angle (B-axis) of the table in addition to the 3-axis axial motion of XYZ, and perform 5 Shaft machining (not shown).

That is, the number of motors that have a correlation in processing varies depending on the machine or processing content. For example, in the case of drilling, the related motors are the spindle motor and the feed axis motor (Z-axis motor). In the case of milling, the related motors are the spindle motor and the feed axis motor ( X-axis motor, Y-axis motor, Z-axis motor).

Figure 5 shows the process of deterioration of processing quality and tool breakage accompanied by tool wear. There is a situation where the tool tip wears as the machining time passes, and the cutting resistance increases, which eventually leads to damage. In addition, if the tool wear continues, the machining accuracy deteriorates, and the specific machining accuracy required for the processed product cannot be maintained.

Figure 6 shows the changes in the force of the main shaft and the feed shaft and the motor torque current in each shaft accompanied by tool wear. As the wear of the tool continues, the blade of the tool becomes easy to slip. Therefore, the load on the spindle motor is reduced. As a result, the torque current of the spindle motor is reduced. On the other hand, the cutting edge of the tool becomes difficult to cut, so the load of the feed axis motor increases, and as a result, the torque current of the feed axis motor increases. That is, by monitoring the changes in the spindle and feed axis motor currents, the tool wear state can be estimated.

Fig. 7 is a schematic diagram of extracting the characteristic quantities of the motor torque current and the motor rotation speed of the main shaft and the feed shaft in each specific processing interval. For example, as the processing time elapses, capture the maximum value, standard deviation, average value, FFT and maximum value of motor rotation speed, standard deviation, average value, FFT, etc. of the torque current in a specific processing section in each processing section Feature amount.

In addition, the torque current value or the rotation speed value of a specific motor or a plurality of motors with related relationships is set as the trigger point of feature quantity extraction, and the feature quantity of the specified interval is calculated, thereby, the sampling frequency or the rotation speed can be adjusted as needed. Computation. As a result, as a result, the amount of data can be drastically reduced, and analysis/diagnosis operations become easier. Furthermore, the type of machining action can be judged based on the current waveform of the feed axis motor, so that more accurate feature quantity extraction can be performed.

Furthermore, if the combination of the characteristic quantities for estimating the degree of tool wear based on the spindle motor current information and the feed axis motor current information is changed according to changes in the tool diameter or machining load, the tool wear degree can be estimated more accurately.

Fig. 8 is a diagram for explaining the estimation method of the tool wear degree (the above-mentioned machine state quantity) using the general linear model method. As an example, the estimation method when the degree of wear is the wear width of the tool blade will be described.

For the measurement tool wear width, based on the feature quantity extracted from the above-mentioned motor current information, an estimation model formula can be constructed using methods such as multivariate analysis. Here, the estimated model formula is an arithmetic formula representing the correspondence relationship between the characteristic quantities exemplified above and the degree of tool wear of the plurality of drive motors. One of the estimated models shown in the general linear model is as described in (Equation 1) below.

Y = a + b × the average value of the torque current of the feed axis motor + c × the maximum value of the feed axis motor rotation speed d × the average value of the spindle motor torque current + e × the standard deviation of the spindle motor rotation speed +... (Equation 1) Here, Y is the degree of tool wear estimated based on the estimated model formula, and a, b, c, d, and e are constants. In other words, the tool wear degree Y can be estimated based on the motor current information having a correlation.

In addition, by adjusting the above-mentioned constants b, c, d, and e (weighting: contribution degree) according to changes in the processing load, the degree of dominance over the target variable Y can be changed, and therefore the estimation accuracy can be improved.

For example, in the drilling process of FIG. 7, in the case of a large-diameter drill (large processing load), the torque current and rotation speed of the spindle motor obviously show a degree of wear. Therefore, in the case of a large-diameter drill (large machining load), the torque current and rotation speed of the spindle motor are further weighted (contribution).

On the other hand, in the case of a small-diameter drill (small processing load), the current and rotation speed of the feed shaft motor are obviously worn. Therefore, in the case of a small-diameter drill (small processing load), if the feed shaft motor current and rotation speed are further weighted (contribution), the drill wear can be estimated with higher accuracy. In this way, the estimation model formula is changed according to the drill as the target machine, whereby the state of the machine can be estimated with better sensitivity.

If (Equation 1) of the estimated model is rewritten as a general functional formula, it becomes the following (Equation 2). Y(I, w, L) = g ₁ (I ₁ , w ₁ ) × k ₁ (L) + g ₂ (I ₂ , w ₂ ) × k ₂ (L) +... (Equation 2) where I is Motor torque current, w is the motor rotation speed, L is the processing load, g ₁ is the function of setting the torque current and rotation speed of motor 1 as variables, and g ₂ is the function of setting the torque current and rotation speed of motor 2 as As a function of variables, k ₁ is a function of motor 1 with machining load as a variable, and k ₂ is a function of motor ₂ with machining load as a variable.

It is possible to use a plurality of characteristic quantities based on current information of a plurality of motors having a correlation and a coefficient (contribution degree) that changes according to the load L to estimate the machine state with better accuracy.

In addition, k ₁ (L), k ₂ (L), ... can also be pre-measured and stored in the data memory according to the machining load or machining mode; the accuracy can be improved based on accumulated data such as machine learning.

In actual use, the limit M _{0 of} tool wear is determined by the processing quality. If there is no estimated error or deviation, the upper limit of the estimated tool wear degree Y becomes Y ₁ corresponding to M ₀ , and if the estimated deviation is considered, the upper limit of the estimated tool wear degree becomes Y ₂ .

That is, if the estimated tool wear degree Y exceeds Y ₂ , the processed product becomes a defective product. By monitoring the estimated tool wear degree Y, the tool replacement time can be accurately grasped. Also, here, the general linear model method is used as an example for explanation. As long as it is a model construction method that expresses the relationship between the degree of tool wear and the feature quantity extracted from the motor current, it does not need to be limited to general linear model methods such as models using statistical methods.

Furthermore, by introducing machine learning to the accumulated data, a more accurate estimation of tool wear can be achieved. Based on the above content, the tool wear can be estimated based on the related motor current information. Therefore, the tool replacement time can be grasped more accurately. In addition, by monitoring the degree of tool wear, it is also possible to prevent tool breakage.

<Action of Example 1> FIG. 9 is a flowchart of the tool wear detection routine executed in the monitoring device 40. The tool wear detection routine is executed in each specific sampling period.

In FIG. 9, the tool wear detection routine is started (START), and the motor current measurement process is executed (step S2). Then, the motor information computing unit 42 of the monitoring device 40 (refer to FIG. 2) acquires the current detection values I _U1 , I _W1 and the current sensor 41 (refer to FIG. 1) of the motor of each axis such as the main axis. The current detection values I _U2 and I _{W2 of} the second axis motor.

Then, after receiving the current detection values of the first axis motor and the second axis motor, the motor information calculation unit 42 calculates the torque current _Ir1 and the rotation speed ω _{rs1 of the} corresponding first axis motor 10-1, the second axis The torque current _Ir2 and the rotation speed ω _{rs2 of the} shaft motor 10-2. That is, the torque current I _r and the mechanical frequency ω _rs of the motor of each axis are output (step S3).

Then, the feature quantity calculation unit 44 extracts the maximum value, standard deviation, average value, FFT and the maximum value, standard deviation, average value, and FFT of the torque current in the specific interval (step S4).

Then, the state estimating unit 45 uses the feature quantity acquired from the data storage unit 46 and the contribution of the related motor corresponding to the load based on the above-mentioned estimation model to perform the calculation of (Equation 1) to calculate the tool wear. (Step S5) The contribution degree of the related motor corresponding to the load used in the estimation of the state estimation unit 45 or the reference value used in the abnormality determination unit 47 is stored in the data storage unit 46 in advance (step S6). The contribution degree or the reference value may also be updated based on the torque current Ir.

Furthermore, the abnormality determination unit 47 compares with the reference value data, determines that the torque current _{Ir is} lower than the set limit value _Ir0 , and determines that it is in the motor rotation state (ω _rs > 0) (YES in step S7), the abnormality determination The section 47 outputs an alarm signal indicating that the tool is in a damaged state to the outside (step S9).

When the abnormality determination unit 47 determines that the degree of wear Y exceeds the set limit value Y ₂ and that it is in the motor rotation state (ω _rs > 0) (YES in step S8), the abnormality determination unit 47 outputs an alarm signal indicating that the tool is in a worn state to Outside (step S10).

In addition, when it does not match either of the two (No in step S7 or step S8), the processing of this routine is ended (END).

<Effects of Embodiment 1> As described above, according to this embodiment, the tool wear state can be detected based on the current values I _U and I _{W of} each of at least two phases of a plurality of motors in correlation. That is, the average service life of the tool can be extended without adding an acceleration sensor or an AE (Acoustic Emission) sensor.

In addition, by visualizing the degree of tool wear, it is possible to save labor for tool maintenance and prevent damage to the tool beforehand. In addition, the abnormality determination unit 47 outputs an alarm signal when it detects the tool wear state. In this way, various abnormalities can be reported to the manager.

According to the first embodiment, based on the current information of a plurality of driving motors, the sensitivity can better determine the abnormality of the machine state.

In addition, if the wear of the tool continues, the machining accuracy will deteriorate, and the specific machining accuracy required by the processed product cannot be maintained. Therefore, the tool used in the cutting process has a large change in life or even breakage due to individual differences. Therefore, like the previous life management methods such as setting the average life as the target and replacing it with a fixed number of processing, if the tool is shorter than the average life, the product may be defective due to reduced processing performance. In the embodiment, such product defects can be avoided.

Moreover, in this embodiment, if it is a tool with a longer than average life, it can prevent the loss of cost caused by replacement before the end of the life.

In addition, it is difficult to add an acceleration sensor or an AE (Acoustic Emission) sensor to detect tool wear in situations where the size of the motor installation site is restricted or under harsh environmental conditions. However, according to this embodiment, this is not necessary. Kind of sensor.

Furthermore, the more the number of sensors increases, the more difficult it is to ensure the reliability of the sensor group, thereby reducing the monitoring accuracy. However, according to this embodiment, no sensors are used, so this problem can be solved.

In addition, since sensors are not used, maintainability and reliability are greatly improved. Specifically, it can not only reduce the maintenance and inspection work of the sensor, but also prevent the failure of the system that accompanies the sensor failure. In addition, the system assembly wiring for the sensor can be reduced, and therefore, the operating cost can be reduced, and troubles such as wiring interference can be eliminated. [Example 2]

FIG. 10 is a block diagram of the motor control system 102 of the second embodiment. In addition, in the following description, the parts corresponding to the parts of the other embodiments described above may be denoted by the same reference numerals, and the description may be omitted.

In FIG. 10, the motor control system 102 includes N (N is a natural number of 3 or more) motors 10-1 to 10-N, and a tool 16 coupled to the motors 10-1 to 10-N via a rotating shaft 14. .

In addition, two (3N in total) current sensors 41 are attached to the U-phase and W-phase of each motor 10-1 to 10-N, and the current detection values I _u1 to I _uN and I _w1 to I _{wN are} It is supplied to the monitoring device 150 (a monitoring device for the degree of tool wear).

Regarding the configuration of the monitoring device 150, as shown in FIG. 11, the monitoring device 40 of the first embodiment (refer to FIG. 2) is provided with N motor information computing units having a correlation instead of two motor information computing units 42 having a correlation. The composition is the same.

For example, there is also a case where the drive motor in the Z-axis, X-axis, or Y-axis as the feed axis in the case of the above-mentioned milling processing is set as a drive motor with a correlation.

In addition, the drive motor for the rotation (C axis) and the inclination angle (B axis) of the worktable in the 5-axis vertical integrated processing machine can be included in the related drive motor. The structure and operation of this embodiment other than the above are substantially the same as those of the first embodiment.

FIG. 12 is a diagram for explaining the situation of extracting the feature quantity related to the machine state from the current information of a plurality of motors. As in Fig. 8 of the first embodiment, for example, it is possible to estimate the state of the machine using a method such as a general linear model of feature quantities. In the case of complex processing, by using multiple motor current information that is beneficial to the processing load, the estimation accuracy of the machine state can be improved. [Example 3]

FIG. 13 is a block diagram of the motor control system 103 of the third embodiment. In addition, in the following description, the parts corresponding to the parts of the other embodiments described above may be denoted by the same reference numerals, and the description may be omitted.

In FIG. 13, the motor control system 103 includes a monitoring device 160 (a monitoring device for the state of the equipment) instead of the monitoring device 40 of the first embodiment (see FIG. 2). The configuration of the monitoring device 160 is substantially the same as that of the monitoring device 40, but the abnormality determination unit 47 outputs a tool wear warning signal (see FIG. 2), and outputs control commands to the command generation unit 32 in the drive device 20 as necessary.

Here, the control command is, for example, instructing the motor 10-1 to stop or accelerate or decelerate, so that, for example, an operation that is most suitable for extending the service life of the tool or maintaining the processing quality can be implemented.

In this way, according to the present embodiment, after the abnormality determination unit 47 detects an abnormality in the machine state (excessive wear and tear of the tool, etc.), it outputs a control command to the control unit 30 to change the control state. As a control command in this case, there are control commands such as stopping the drive motor or reducing the rotation speed. Thereby, the control state in the control unit 30 can be changed to an appropriate state. [Example 4]

FIG. 14 is a block diagram of the motor control system 104 of the fourth embodiment. In addition, in the following description, parts corresponding to the parts of the other embodiments described above may be denoted by the same reference numerals, and the description thereof may be omitted.

In FIG. 14, the motor control system 104 is provided with a monitoring device 170 (a monitoring device for the state of the machine) instead of the monitoring device 40 of the second embodiment (see FIG. 11). The configuration of the monitoring device 170 is substantially the same as that of the monitoring device 150, but the abnormality determination unit 47 outputs a machine state abnormality alarm signal (see FIG. 11), and outputs control commands to the command generation unit 32 in the drive device 20 as necessary.

Here, the control command is, for example, instructing the motor 10-1 to stop or accelerate or decelerate, so that an operation that is most suitable for extending the service life of the tool or maintaining the processing quality can be implemented.

In this way, according to the present embodiment, after the abnormality determination unit 47 detects an abnormality in the machine state (excessive wear and tear of the tool, etc.), it outputs a control command to the control unit 30 to change the control state. Thereby, the control state in the control unit 30 can be changed to an appropriate state. [Example 5]

FIG. 15 is a block diagram of the motor control system 105 of the fifth embodiment. In addition, in the following description, parts corresponding to the parts of the other embodiments described above may be denoted by the same reference numerals, and the description thereof may be omitted.

In FIG. 15, the motor control system 105 includes a driving and monitoring device 180, motors 10-1 to 10-N, and a tool 16 coupled via a rotating shaft 14. The driving and monitoring device 180 includes a control unit 30, an inverter 22, and a monitoring device 190 (detection of abnormal machine status).

The configuration of the control unit 30 and the inverter 22 is the same as that of the first embodiment (see FIG. 1), and the configuration of the monitoring device 190 is the same as that of the monitoring device 170 (see FIG. 14) of the fourth embodiment. Therefore, the driving and monitoring device 180 of this embodiment has the function of combining the functions of the driving device 20 and the monitoring device 170 in the fourth embodiment. Furthermore, this embodiment can also be constructed by adding a monitoring device 190 to the existing driving device 20 (refer to FIG. 14). [Example 6]

FIG. 16 is a block diagram of the motor-driven servo amplifier 106 of the sixth embodiment. In addition, in the following description, parts corresponding to the parts of the other embodiments described above may be denoted by the same reference numerals, and the description thereof may be omitted.

In FIG. 16, the servo amplifier 106 includes a motor 10-1, a rotating shaft 14, a tool 16, a servo amplifier 106, and a monitoring device 210. Furthermore, current sensors 41 are installed on the U-phase and W-phase of the motor 10-1, and the current detection values I _u1 and I _w1 of the motor 10-1 and the current detection values I _u2 and I of the motor 10-2 from the servo amplifier 2 _{w2 is} supplied to the monitoring device 210 (the monitoring device of the machine state).

The configuration of the monitoring device 210 is the same as the configuration in which the monitoring device 40 of the first embodiment (refer to FIG. 2) is provided with two motor information computing units 42 as shown in FIG. The structure and operation of this embodiment other than the above are substantially the same as those of the first embodiment.

In addition, in this embodiment, the embodiment in which the function of detecting abnormal state of the machine is incorporated in the servo amplifier that supplies current to the motor is described, but if the function of detecting abnormal state of the machine is incorporated into the inverter that supplies current to the motor Function, it can also construct an inverter (not shown) for detecting the wear status of tools.

Based on the above content, the state of the machine can be estimated based on multiple motor current information related to each other. Therefore, it is possible to more accurately grasp the time of replacement or maintenance of the machine's tools. [Example 7]

FIG. 17 is a block diagram of the motor-driven servo amplifier 107 of the seventh embodiment. In addition, in the following description, parts corresponding to the parts of the other embodiments described above may be denoted by the same reference numerals, and the description thereof may be omitted.

In FIG. 17, the servo amplifier 107 includes N (N is a natural number of 3 or more) motors 10-1 to 10-N, a rotating shaft 14, a tool 16, a servo amplifier 107, and a monitoring device 220.

The configuration of the monitoring device 220 is the same as the configuration in which N motor information calculation units are provided as shown in FIG. 11. The structure and operation of this embodiment other than the above are substantially the same as those of the first embodiment.

According to the above, the state of the machine can be estimated based on the current information of a plurality of motors that have a correlation. Therefore, it is possible to more accurately grasp the time of replacement or maintenance of the tools of the machine. [Example 8]

FIG. 18 is a block diagram of the industrial controller 108 of the eighth embodiment. The industrial controller 108 is combined with the production line or equipment of the networked factory to realize the seamless vertical integration of robot control or collection of equipment and machine data from various sensors with the upper information system. Moreover, the industrial controller 108 combines the functions of an industrial computer and the open integrated development environment of a PLC (programmable logic controller) into one. Not only control the equipment in the factory, but also realize the optimization of the whole factory or the whole supply chain by collecting and analyzing information.

In addition, in the following description, parts corresponding to the parts of the other embodiments described above may be denoted by the same reference numerals, and the description thereof may be omitted.

In FIG. 18, the industrial controller 108 includes an information collection unit 240 and a monitoring device 230. In addition, the current detection values I _u1 to I _uN and I _w1 to I _{wN of} the motors 10-1 to 10 -N are supplied to the information collection unit 240 from the inverter or the servo amplifier.

The configuration of the monitoring device 230 is the same as the configuration in which N motor information calculation units are provided as shown in FIG. 11. The structure and operation of this embodiment other than the above are substantially the same as those of the first embodiment.

According to the above, the state of multiple machines can be estimated in the industrial controller based on the current information of multiple motors connected to the network. Therefore, the optimization or maintenance of each tool replacement period can be realized more efficiently. The labor saving. [Example 9]

Figure 19 is a schematic diagram of the machine tool of the ninth embodiment. In addition, in the following description, parts corresponding to the parts of the other embodiments described above may be denoted by the same reference numerals, and the description thereof may be omitted.

In FIG. 19, the machine tool 109 is equipped with a Z-axis motor servo amplifier, a spindle motor inverter, an X-axis motor servo amplifier, a Y-axis motor servo amplifier, and a monitoring device 250 in its control section. In addition, the current detection value of each axis motor is supplied to the monitoring device from the servo amplifier or the inverter. It can also output tool wear information from the monitoring device to the control operation screen (panel) of the machine tool, and display an alarm or warning message (not shown).

According to the above, the wear degree of the tool can be estimated based on the current information of the motors of the multiple axes of the machine tool. Therefore, the optimization of each tool replacement period or the labor saving of maintenance can be realized more efficiently.

It is not limited to the above-mentioned embodiment, and various changes can be made. The above-mentioned embodiments are exemplified in order to easily understand the present invention, and are not necessarily limited to those having all the constitutions described.

In addition, a part of the configuration of a certain embodiment may be replaced with a configuration of another embodiment, or a configuration of another embodiment may be added to the configuration of a certain embodiment. In addition, it is possible to delete a part of the configuration of each embodiment, add or replace other configurations.

In addition, the control lines or information lines shown in the figure represent what is required for explanation, and do not represent all the control lines or information lines required on the product. In fact, it is also possible to consider connecting almost all components to each other. The possible changes to the above-mentioned embodiment are, for example, those described below.

(1) The hardware of the control unit 30 and the monitoring devices 40, 150, 160, 170, 190, 210, 220, 230, and 250 in the above-mentioned embodiment can be realized by an ordinary computer. The algorithm shown, the program corresponding to the flowchart shown in Fig. 9, etc. are stored in a memory medium or distributed via a transmission path.

(2) The algorithm shown in Fig. 2 and Fig. 3 or the flowchart shown in Fig. 9 are described as software processing of the program in each embodiment. However, part or all of it can be replaced with ASIC (Application Specific Integrated Circuit), IC (Integrated Circuit) for specific purposes, or FPGA (field-programmable gate array). Domain programmable gate array) and other hardware processing.

(3) A plurality of inverters are installed in the configuration of Fig. 10 and the like, but only one inverter 22 may be installed.

In the above-mentioned embodiments, the situation of adapting to the processing of the use of tools is mainly explained, but not only in the processing of tools, but also in the operation of robots, it can also be effectively used to detect abnormalities in the state of machines including robots. .

In the above embodiments, the application examples of the inverter and the servo amplifier are described, but it can also be applied to power conversion devices such as DCBL (Direct Current Brushless) controllers.

10: Motor 10-1~10-N: Motor 14: Rotating shaft 16: Tool 20: Drive 22: Inverter 24: Current sensor 30: Control part 32: Command generation part 33: Deviation calculation part 34: Vector Control unit 36: dq/3Φ conversion unit 38: 3Φ/dq conversion unit 40, 150, 160, 170, 190, 210, 220, 230, 250: Monitoring device 41: Current sensor 42: Motor information calculation unit 42-1~42-N: Motor information calculation unit 44: Feature quantity calculation unit 45: State estimation unit 46: Data storage unit 47: Abnormality determination unit 52: 3Φ/αβ converter 54: Arctangent converter 56: Subtractor 60: Phase calculator 62: Multiplier 64: Multiplier 66: Integrator 68 : Adder 70: Rotational coordinate converter 72: Integrator 74: Multiplier 101: Motor control system 102: Motor control system 103: Motor control system 104: Motor control system 105: Motor control system 106: Motor drive servo amplifier 107: Motor drive servo amplifier 108: Industrial controller 109: Machine tool 180: Monitoring device 240: Information collection unit τ*: Torque command value I _α , I _β : AC current I _d : Excitation current detection value I _q : Torque Current detection values I _r1 , I _r2 : Motor torque current I _rN : Torque current ω _1s : Frequency signal ω _rs : Mechanical frequency ω _rs1 , ω _rs2 : Mechanical frequency I _d *: Excitation current command value I _q *: Rotation Moment current command value I _r : Direct current I _U , I _W : Current detection value I _U1 , I _W1 : Current detection value I _U2 , I _W2 : Current detection value I _Us : Current detection value I _UN : Current detection value I _WN : Current detection value I _Ws : Current detection value KiPLL: Integral gain KpPLL: Proportional gain M ₀ : Tool wear limit PLL_I: Integral signal PLL_P: Proportional signal V _d *: Excitation voltage command value V _q *: Torque voltage command Value θ _i : AC current phase angle θ _i *: AC current phase angle detection value

Figure 1 is a block diagram of the motor control system in the first embodiment. Figure 2 is a block diagram of the monitoring device in the first embodiment. FIG. 3 is a block diagram of the motor information computing unit in the first embodiment. Fig. 4 is a diagram showing a configuration example of the machine tool in the first embodiment. FIG. 5 is a diagram showing the mechanism of the deterioration of processing quality and tool damage caused by tool wear in Example 1. FIG. FIG. 6 is a graph showing changes in motor current accompanying tool wear in Example 1. FIG. FIG. 7 is a schematic diagram of feature quantity extraction from motor information in Embodiment 1. FIG. 8 is a diagram of the method of estimating tool wear based on motor current information in Embodiment 1. FIG. 9 is a flowchart of the routine for detecting tool wear in the first embodiment. Fig. 10 is a block diagram of the motor control system in the second embodiment. Fig. 11 is a block diagram of the monitoring device in the second embodiment. FIG. 12 is a schematic diagram of feature quantity extraction when a plurality of machining axis motors are used in the second embodiment. FIG. 13 is a block diagram of the motor control system in the third embodiment. FIG. 14 is a block diagram of the motor control system in the fourth embodiment. Fig. 15 is a block diagram of the motor control system in the fifth embodiment. Fig. 16 is a block diagram of the motor-driven servo amplifier in the sixth embodiment. Fig. 17 is a block diagram of the motor-driven servo amplifier in the seventh embodiment. Fig. 18 is a block diagram of the industrial controller in the eighth embodiment. Figure 19 is a schematic diagram of the machine tool in the ninth embodiment.

40: Monitoring device

42: Motor Information Computing Department

44: Feature calculation unit

45: State Estimation Department

46: Data Memory Department

47: Abnormality Judgment Department

I _r1 , I _r2 : motor torque current

ω _rs1 ,ω _rs2 : mechanical frequency

I _U1 , I _W1 : current detection value

I _U2 , I _W2 : current detection value

Claims (12)

A monitoring device, characterized by having: A current sensor, which detects two-phase current information for each of a plurality of driving motors; A motor information calculation unit, which calculates the corresponding torque current or rotation speed of the drive motor according to the current information; A feature quantity calculation unit that computes feature quantities related to the torque current or the rotation speed in a plurality of the drive motors; A state estimating unit, which estimates the machine state based on the above-mentioned characteristic quantities of the above-mentioned driving motors in a correlation relationship among a plurality of the above-mentioned driving motors; Data storage unit, which records the reference value data; and The abnormality determination unit determines the abnormal state based on the estimated state of the machine and the reference value data. Such as the monitoring device of claim 1, where The above-mentioned machine state indicates the degree of wear of the tool driven by the above-mentioned drive motor. Such as the monitoring device of claim 1, where The characteristic quantity is the maximum value, standard deviation, average value, or FFT of the torque current of the drive motor or the rotation speed of the drive motor. Such as the monitoring device of claim 1, where The above state estimation department The machine state is calculated based on a plurality of the feature quantities in a correlation relationship with the machine state among the plurality of the feature quantities of the drive motors, and a weighted contribution to the feature quantity. Such as the monitoring device of claim 4, where The above drive motor system Including spindle motor and feed axis motor, The above state estimation department The machine state is calculated based on the characteristic quantities of the spindle motor and the feed axis motor in the correlation relationship. Such as the monitoring device of claim 1, where The above-mentioned abnormality determination department When an abnormal situation is detected, a control command is output to the control unit that controls the above-mentioned drive motor. Such as the monitoring device of claim 1, where The above-mentioned feature amount calculation unit Set the current value or the rotation speed value of one or a plurality of the above-mentioned driving motors with a correlation relationship as a trigger point for capturing the above-mentioned characteristic quantity, and calculate the above-mentioned characteristic quantity in a specified interval. Such as the monitoring device of claim 4, where The above state estimation department The above-mentioned machine state is calculated by including the above-mentioned characteristic quantities of the above-mentioned drive motor of the rotating shaft of the worktable of the fixed processed product and the above-mentioned drive motor of the axis of the tilt angle. A servo amplifier is characterized by having a monitoring device such as claim 1 for inputting the above-mentioned current information from other servo amplifiers. An industrial controller characterized by: An information collection unit, which collects the above-mentioned current information from a plurality of servo amplifiers or inverters; and Such as the monitoring device of claim 1. A machine tool characterized by: In its control department: Inverters or servo amplifiers for motors with multiple axes; and Such as the monitoring device of claim 1. A monitoring method characterized by: For each of a plurality of drive motors, detect two-phase current information, According to the above current information, calculate the corresponding torque current or rotation speed of the above drive motor, Calculate the characteristic quantities related to the torque current or the rotation speed in the plurality of drive motors, Based on the above-mentioned characteristic quantities of the above-mentioned driving motors in correlation among the plurality of the above-mentioned driving motors, the machine state is estimated, Determine the abnormal state based on the pre-recorded reference value data and the estimated machine state.

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