JP2004350387A - Method and device for detecting abnormality of motor - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a means for detecting abnormality of a motor capable of surely detecting abnormality of a motor and also capable of predicting failure of the motor. <P>SOLUTION: Sampling data generated from a sensor signal are interpolated at an interpolation processor 51, and stored in a sampling data storing part 61. After the data of prescribed number of pieces are stored, they are read out of the sampling data storing part 61, and inputted in a calculation part 52. At the same time, a reference data stored in advance in a reference data storing part 62 is also inputted in the calculation part 52. The calculation part 52 performs correlation operation between the sampling data and the reference data. The calculation result is determined at a determination part 53, and an alert is outputted if an abnormality exists. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to a method and an apparatus for detecting an abnormality of a motor, and more particularly to a method and an apparatus for detecting an abnormality of a DC motor and predicting a failure.
[0002]
[Prior art]
As a motor, particularly a control motor, a brushless DC motor is often used because of its high efficiency and stability. Conventionally, in order to detect an abnormality of a DC motor such as a brushless motor, the number of rotations, a driving voltage or a driving current of the motor is monitored, and an alarm is issued when the motor speed exceeds a set value and is out of a normal operation range. I was There is also a conventional example in which a current is supplied before the brushless motor is started to determine whether or not normal operation is possible (see Patent Document 1).
[0003]
[Patent Document 1]
JP-A-9-322589
[Problems to be solved by the invention]
However, in the abnormality determination by simply comparing the rotational speed and the drive current with a set threshold value, even if an abnormality is detected during the operation of the motor, it may be too late if the failure is fatal. Further, erroneous detection or omission of detection may occur due to current fluctuation in a short time or noise generated inside or outside the motor. Particularly, in the case of a motor used in an unmanned facility such as a factory or a mobile phone base station that operates for 24 hours, an abnormality occurs. Further, in the case where the normal operation is checked before the motor is started, a failure during the operation cannot be dealt with.
[0005]
The present invention has been made in view of the above problems, and provides an abnormality detection method and apparatus capable of reliably detecting an abnormality of a motor during motor operation, prompting replacement and maintenance of the motor, and preventing a catastrophic failure of the entire system. The purpose is to provide.
[0006]
[Means for Solving the Problems]
The present invention, in order to achieve the above object, stores the current value of the motor as time-series reference current data, performs an operation based on the detected current data detected during motor operation and the reference current data, Provided are a motor abnormality detection device and method for detecting a motor abnormality from a calculation result.
[0007]
This calculation may include a calculation for obtaining a correlation value, and the reference current data may be current data when the motor is normal or current data when the motor is abnormal.
Further, according to the present invention, the starting point of the difference calculation is determined based on the correlation value calculated from the detected current data detected during motor operation and the reference current data and the time at which the detected current data was obtained, By calculating the difference between the data and the reference current data, it is also possible to predict a motor abnormality.
[0008]
According to the present invention, the reference data is stored in advance in a time series, and the calculation of the reference data and the detection data is performed, so that the motor abnormality can be reliably detected and the failure can be predicted.
[0009]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings.
FIG. 1 is an outline of one embodiment of a motor abnormality detection device according to the present invention.
When the motor 1 starts rotating by receiving a command value of the number of rotations from the command value output circuit 55 of the microprocessor 5, the current sensor 2 detects the motor current and outputs a sensor signal corresponding to the current value. I do. This sensor signal (a voltage signal in the present embodiment) is amplified by an amplifier 3 composed of an OP amplifier and input to an A / D converter 4. The A / D converter 4 samples the sensor signal with a sampling clock input from the clock clock generation circuit 54 of the MPU 5. The sampling clock here can be changed and may be a real-time clock.
[0010]
The sampling data thus generated from the sensor signal is input to the microprocessor 5. The microprocessor 5 stores the sampling data in the sampling data storage unit 61 of the storage device 6 after performing interpolation processing. When the storage of the predetermined number of data is completed, these are read from the sampling data storage unit 61 and input to the arithmetic unit 52. At the same time, reference data (template) previously stored in the reference data storage unit 62 of the storage device 62 is also input to the calculation unit 52. The reference data is obtained in the same manner as the method of obtaining the sampling data during normal operation of the motor. The calculation unit 52 performs a correlation calculation between the sampling data and the reference data. The calculation result is stored in the calculation result storage unit 63 of the sequential storage device 62. When the result of the correlation operation at a predetermined number of data points is stored, the determination unit 53 of the MPU 5 predicts the presence or absence of a motor abnormality or a failure based on the value. If there is an abnormality or the like, a notification signal is output.
[0011]
One of the features of the present invention is that a current value of a motor drive current at a normal time is stored in time series as reference data, that is, template data. The reference data set is a data set obtained in advance during normal operation of the motor, and a plurality of data sets can be stored according to the control mode of the motor.
[0012]
Next, a description will be given of acquisition of reference data. The method of obtaining the reference data is the same as the method of obtaining sampling data for detecting a motor abnormality.
[0013]
FIG. 2A shows an example of a current waveform during normal operation of the brushless DC motor. The vertical axis is the current value, and the horizontal axis is time. In this example, PWM driving is performed. After the motor starts rotating, the motor stops after an acceleration period (A), a constant speed period (B), and a deceleration period (C) as shown in FIG. This is an example, and various types of motor control can be performed according to command values given to the motor. The sampling in the A / D converter is indicated by a circle in FIG. In this example, the current waveform may not be faithfully sampled at some sampling points due to the PWM drive of the motor. Therefore, as shown in FIG. 3A, an interpolation process using a quadratic function is performed to smoothly connect acceleration, constant speed, and deceleration. It is preferable that the reference data is divided into an acceleration period (A), a constant speed period (B), and a deceleration period (C), and each pattern is cut out and stored in a storage device. As an example, FIG. 3B shows a pattern of the extracted acceleration period (A). When the data is cut out in this manner, the reference data storage unit has reference data, that is, template data for each of acceleration, constant speed, and deceleration patterns.
[0014]
The reference data obtained after the interpolation processing is set as a set of the number of times of sampling, a sampling time, a motor command value, and a current value for each motor as shown in FIG. Is stored in In FIG. 4, cutting is not performed for each pattern of acceleration, constant speed, and deceleration, but cutting is easy.
[0015]
Next, with reference to FIGS. 5 and 6, a description will be given of a motor abnormality detection operation during motor operation in the present embodiment. FIG. 5 is a flowchart showing the entire motor abnormality detection. Steps S1 to S4 show steps until the input sampling signal is written to the sampling data storage unit 61 of the storage device. Step S5 is a step of the arithmetic processing, and FIG. 6 shows a flow of the arithmetic processing step. Steps S6 to S9 are steps until an abnormality alarm is output.
[0016]
In the abnormality detection operation, the reference data is stored in the storage device in advance as described above. First, a command value is input to drive the motor M, a current flows based on the command value, and the motor M starts rotating. As a result, the current sensor 1 senses the power supply current, and outputs a signal corresponding to the power supply current.
[0017]
In step S1, the output signal of the current sensor amplified by the amplifier 3 is input to the A / D converter 4, where it is sampled by a predetermined sampling pulse and converted into digital value sampling data.
[0018]
In step S2, the sampling data input to the interpolation processing unit 51 of the MPU 5 is subjected to a data interpolation process using a quadratic function so as to smoothly connect the sampled values.
[0019]
In step S3, the interpolated data is written to the sampling data storage unit 61 of the storage device 6. The storage device may be any device that can store data, such as a semiconductor memory or a hard disk, but a high-speed storage device is desirable.
[0020]
In step S4, it is confirmed whether the writing of a predetermined number of data has been completed. If the predetermined number of data has not been written yet, the process returns to step S1 and repeats this process. If a predetermined number of data required for the calculation in the next step are available, the process proceeds to the next step S5, where the predetermined calculation is executed.
[0021]
FIG. 6 shows a flow of the correlation calculation of the present embodiment.
In step S501, when starting a correlation operation, an initial pointer indicating an initial position of the operation is set. In the present embodiment, since the current values along the time waveforms of acceleration, constant speed, and deceleration are held as reference data extracted as an acceleration pattern, a constant speed pattern, and a deceleration pattern, the correlation calculation is performed using these three patterns. It can be carried out. With respect to the sampling data as input data, it is possible to know in which stage the input data is in acceleration, constant speed, or deceleration based on the current command value, and to divide the data into patterns, so that an efficient correlation operation can be performed. .
[0022]
In step S502, a predetermined number of sampling data written in the sampling data storage unit 61 in the previous step is read, an autocorrelation of the sampling data is obtained, and the result is written in the storage device. Since the correlation calculation itself is known, the description is omitted.
[0023]
In step S503, the reference data (template) stored in the storage device in advance is read, the autocorrelation is obtained, and the data is written to the storage device.
[0024]
In step S504, the input data and the reference data are read from the memory and cross-correlated.
[0025]
In step S505, a normalized correlation value is obtained from the auto-correlation value and the cross-correlation value obtained in steps S502, S503, and S503, and written to the calculation result storage unit 63 of the storage device 61.
[0026]
Next, in step S506, the pointer is incremented and the process proceeds to the next point. Next, in step S507, it is determined whether the predetermined number has been completed. If it is not the end point, the process returns to step S501, and a correlation calculation is performed at the next point, and a normalized correlation value at this point is calculated. If it is the end point, the correlation calculation ends and the process proceeds to the next step S6 (FIG. 5).
[0027]
In step 6, the calculation result at a predetermined number of points stored in the calculation result storage unit 63 of the storage device 6 is read by the determination unit, and it is determined whether the normalized correlation value, which is the calculation result, is within an allowable range. I do. As a result, if it is within the range, the motor is determined to be operating normally, and the process returns to the start.
[0028]
If the calculation result exceeds the allowable range, it is determined that an abnormality has occurred in the motor, and an alarm output is output.
[0029]
FIG. 10 shows the distribution of the normalized correlation values stored in the calculation result section. This is an example in which a range of 3σ around the highest correlation value (= 1) is an allowable value. A value exceeding 3σ indicates an abnormality.
[0030]
As described above, according to the present invention, the current data when the motor is normal is held as the reference data, and the correlation operation is performed with the reference data to determine the abnormality, so that it is possible to generate reliable data. In addition, erroneous detection can be prevented. In addition, it is possible to know the tendency of the current value to change along with the operation time, and it is possible to predict a failure. Further, by having the reference data divided into the acceleration pattern, the constant speed pattern, and the deceleration pattern, the correlation calculation can be performed efficiently.
[0031]
(Second embodiment)
The second embodiment further utilizes the fact that the present invention holds, as reference data, a current value along a time axis such as acceleration, constant speed, and deceleration of a motor. To obtain the actual time at the time of sampling. Then, the MPU 5 does not use the normalized correlation value obtained as a result of the calculation as judgment data, but obtains a reference start point from the normalized correlation value, and sequentially receives input data and reference data from the start point. Is calculated, and a future failure is predicted from the distribution of the difference calculation result.
[0032]
The second embodiment is a modification of a part of the flow of the first embodiment, and will be described based on the flows shown in FIGS.
[0033]
In the second embodiment, the sensor signal from the sensor 2 in step 1 is sampled by the A / D converter 4 in steps S1 to S4 until the sampling data is obtained and stored in the sampling data storage unit. At this time, a real-time clock is used instead of a simple sampling clock. That is, the real time of sampling is obtained together with the sampling signal, and the sampling time is stored in the sampling data storage unit 61 together with the sampling signal.
[0034]
In the stored data, for example, as shown in FIG. 8, the actual time at which the sampling was performed is a set of sampling data captured as the sampling time.
[0035]
Next, the steps of the calculation processing in step 5 are the same steps up to step S507 shown in FIG. 6, and then steps S508 to S511 shown in FIG. 9 are added. This added step is to take the difference between the sampling data and the reference data, and to see how far the reference data and the actual sampling data are actually separated and what the tendency of the difference is. Done in
[0036]
That is, after all the steps for obtaining the normalized correlation values are completed (step 507 (FIG. 6)), in step S508 (FIG. 9), the calculated normalized correlation values are read from all the memories and the normalized correlation values are determined. The sampling data having the maximum value when the value is equal to or more than the value is obtained, and this is set as the starting point of the difference calculation. The point where the normalized correlation value becomes the maximum is the point most similar to the reference data, so it can be said that it is the most normal data in the data, and the start point to check whether it has a tendency to approach the abnormal state Suitable to do.
[0037]
Next, at step 510, a difference is obtained between the sampling data at the determined start point and the reference value, and the calculation result is written to the calculation result storage unit.
[0038]
Next, in step 510, the calculation point is incremented from the start point and advanced by one.
[0039]
In the next step S511, it is determined whether or not it is a point to be calculated. Since the point next to the start point is not usually the final point, the process returns to step 509 to execute the difference calculation and write the result to the calculation result storage unit. This operation is repeated, and if there are no more points to be operated next, the operation processing step 5 ends, and the process proceeds to the next step S6 (FIG. 5).
[0040]
In step 6, a predetermined number of difference values stored in the calculation result storage unit as a result of the calculation are read from the memory.
[0041]
Next, in step 7, a difference as a calculation result is determined. FIG. 10 shows an example of a distribution of difference values between the input sampling data and the reference data, which is to be determined to be abnormal. A deviation is taken on the vertical axis centering on the difference value 0, and the line of 3σ is indicated by a broken line. The horizontal axis is the sampling point. As can be seen on the right side of the figure, sample points are concentrated and continuous in the vicinity of the 3σ (same for -3σ) line, and as shown on the left side of the figure, the sample points are continuous for a predetermined period. And rising (same as falling). If any one of these cases occurs, it can be predicted that a failure will occur eventually. Since the criteria for such determination differ depending on various factors such as the motor device itself, the motor arrangement environment, the control method, and the like, the determination criteria can be set in consideration of the operation status. For example, even if the sample point is not close to the 3σ line, if the sample point is concentrated and continuous on one side of the center line, a failure can be predicted at a certain point.
[0042]
(Third embodiment)
In the first and second embodiments, the reference template, which is the reference data, is configured from the data in the normal state. In the third embodiment, the data in the abnormal state is used as the reference template.
[0043]
FIG. 11A shows a current waveform at the time of restraint, and FIG. 11B shows a waveform of a short-circuit current and a surge current at the time of abnormality. In the third embodiment, data obtained from these typical abnormal current waveforms is adopted as reference data. FIG. 11C shows an example of template data obtained from the current waveform at the time of constraint.
[0044]
When the motor starts the restricted operation, as shown in FIG. 11A, a pulse-shaped current flows at intervals of, for example, about 4 seconds. When the motor is used in an environment that is likely to be in a restricted operation, it may be desirable to reliably detect that the operation is a restricted operation. Also, when a component of the control circuit breaks down, an instantaneous short-circuit current or surge current is often generated in the motor from experience. In such a case, data of a current value in the case of a typical motor abnormality is obtained, stored in a storage device as reference data, and a correlation operation similar to that described in the above embodiment is performed. This makes it possible to know what kind of failure has actually occurred. In the third embodiment, if the normalized correlation value is calculated to be high, it is determined that there is an abnormality similar to the reference data.
[0045]
As described above, as an embodiment of the present invention, an example in which the correlation calculation process is performed has been described, but the present invention is not limited to the correlation calculation. For example, the sampling data sequentially obtained during the motor operation and the reference data (template) stored in the storage device may be determined for a predetermined period, and the difference calculation may be performed in that period. By subtracting the normal data from the difference, an effect is obtained that abnormal data is clearly detected.
[Brief description of the drawings]
FIG. 1 is a diagram illustrating a motor abnormality detection device according to an embodiment of the present invention.
FIG. 2A is a diagram illustrating an example of a motor current detected by a sensor, and FIG. 2B is a diagram illustrating sampling of a motor current.
3A is a diagram illustrating a secondary interpolation process of sampling data of a motor current, and FIG. 3B is a diagram illustrating reference data after the secondary interpolation process.
FIG. 4 is a diagram showing reference data stored in a storage device.
FIG. 5 is a flowchart showing an operation flow of the embodiment of the present invention.
FIG. 6 is a flowchart showing the contents of a calculation process in the operation flow of the embodiment of the present invention.
FIG. 7 is a diagram illustrating an example of a normalized correlation value of a calculation result.
FIG. 8 is a diagram illustrating an example of sampling data obtained in a second embodiment of the present invention.
FIG. 9 is a flowchart illustrating a difference calculation flow according to the second embodiment of the present invention.
FIG. 10 is a diagram illustrating an example of a distribution of a difference that is a calculation result according to the second embodiment of the present invention.
11A is a diagram showing a constrained current waveform among the abnormal current waveforms, FIG. 11B is a diagram showing a short-circuit current or surge current waveform among the abnormal current waveforms, and FIG. 14 is an example of reference data obtained from a constrained current waveform used in the third embodiment of the present invention.
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 1 ... Brushless DC motor 2 ... Current sensor 3 ... Amplifier 4 ... A / D converter 5 ... Microprocessor 51 ... Interpolation processing part 52 ... Calculation part 53 ... Judgment part 54 ... Clock generation part 55 ... Command value output part 6 ... Storage Device 61: sampling data storage unit 62: reference data storage unit 63: operation result storage unit

Claims (12)

Storage means for storing a current value of the motor as time-series reference current data; and calculation means for performing calculation based on detected current data detected during motor operation and the reference current data. A motor abnormality detection device for detecting a motor abnormality from a result. The motor abnormality detection device according to claim 1, wherein the calculation is a calculation for obtaining a correlation value. 3. The motor abnormality detecting device according to claim 1, wherein the reference current data is current data when the motor is normal. The motor abnormality detection device according to claim 3, wherein the reference current data is stored as an acceleration pattern, a constant speed pattern, and a deceleration pattern. The motor abnormality detecting device according to claim 1, wherein the reference current data is current data when the motor is abnormal. Storage means for storing the current value of the motor as reference current data; and calculation means for performing calculation based on the detected current data detected during motor operation and the reference current data, wherein the correlation calculated by the calculation means is calculated. A start point of a difference calculation between the detected current data and the reference current data is determined based on the value and the time at which the detected current data is obtained, and a motor abnormality is determined based on a difference calculation result executed from the start point by the calculation unit. A motor abnormality detection device characterized by predicting. A step of performing a predetermined calculation based on detected current data detected during motor operation and time-series reference current data obtained in advance, and a step of detecting a motor abnormality based on the calculation result A motor abnormality detection method characterized by the above-mentioned. The motor abnormality detection method according to claim 7, wherein the step of executing the predetermined calculation is a step of calculating a correlation value. 9. The motor abnormality detecting method according to claim 7, wherein the reference current data is current data when the motor is normal. The motor abnormality detecting method according to claim 9, wherein the reference current data is stored as an acceleration pattern, a constant speed pattern, and a deceleration pattern. The motor abnormality detection method according to claim 9, wherein the reference current data is current data when the motor is abnormal. Acquiring detection data from a detection current value detected during motor operation, and simultaneously obtaining an acquisition time;
Calculating a correlation value from the detection data and reference data obtained in advance,
Determining a starting point of the difference calculation based on the correlation value and the detection time;
Calculating a difference between the detection data and the reference data from a start point of the difference calculation to obtain a distribution of the difference,
A motor abnormality detection method, wherein a motor abnormality is predicted from the difference distribution.

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