CN117169717A - Motor health assessment method and device based on single chip microcomputer and storage medium - Google Patents
Motor health assessment method and device based on single chip microcomputer and storage medium Download PDFInfo
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- CN117169717A CN117169717A CN202311165715.8A CN202311165715A CN117169717A CN 117169717 A CN117169717 A CN 117169717A CN 202311165715 A CN202311165715 A CN 202311165715A CN 117169717 A CN117169717 A CN 117169717A
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- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000003860 storage Methods 0.000 title claims abstract description 8
- 238000012544 monitoring process Methods 0.000 claims abstract description 38
- 238000004804 winding Methods 0.000 claims abstract description 16
- 238000009826 distribution Methods 0.000 claims abstract description 9
- 238000011156 evaluation Methods 0.000 claims abstract description 7
- 238000007781 pre-processing Methods 0.000 claims abstract description 7
- 230000004927 fusion Effects 0.000 claims abstract description 5
- 238000012163 sequencing technique Methods 0.000 claims abstract description 5
- 238000012545 processing Methods 0.000 claims description 11
- 230000007613 environmental effect Effects 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 230000008054 signal transmission Effects 0.000 claims description 3
- 230000008030 elimination Effects 0.000 claims description 2
- 238000003379 elimination reaction Methods 0.000 claims description 2
- 230000002159 abnormal effect Effects 0.000 description 4
- 238000003745 diagnosis Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000012631 diagnostic technique Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
Abstract
The invention discloses a motor health assessment method, a motor health assessment device and a storage medium based on a singlechip, wherein a sensor is used for acquiring operation parameters of a motor; preprocessing and extracting characteristics of the acquired operation parameters; respectively carrying out trust value determination on single sensor alarms in certain faults to obtain a sensor monitoring value with high trust degree; carrying out regularized fusion with an expert knowledge base and carrying out weight distribution on fault types; dividing the health degree grade aiming at the motor, and carrying out health degree grade assessment on the motor according to the weight distribution of each fault; and carrying out fault prediction sequencing on each fault of the motor according to the type weight so as to predict and locate fault types and fault points. According to the invention, through the acquisition of each operation parameter of the motor, four main faults including the rotor broken bar fault, the stator winding fault, the bearing fault and the motor shaft fault are identified to execute the health grade evaluation of the motor, so that the timely prediction and positioning of the motor faults are realized, and the guarantee is provided for the efficient and safe operation of the motor.
Description
Technical Field
The invention belongs to the technical field of motor health assessment, and particularly relates to a motor health assessment method and device based on a single chip microcomputer and a storage medium.
Background
Asynchronous motors are very widely applied to production and living because of excellent performance, but the faults of the motors occur due to various reasons, equipment damage is caused, and economic loss is caused, so that on-line health evaluation of the motors is very important, and the on-line health evaluation method has great economic value and social significance.
Multiple sensors and multiple diagnostic techniques are commonly employed in motor fault diagnostic systems. However, the conventional diagnosis judgment structure is still carried out based on a single sensor and a single parameter characteristic. The diagnosis center monitors various parameters of the equipment by using various similar and different sensors, and sets the characteristic parameters and the alarm threshold value of each fault. A fault is deemed to occur when a certain class of characteristic parameter exceeds a threshold. Because of the coupling of motor operation parameters, a detection and diagnosis technology can often identify various faults, and each sensor information easily generates contradictory diagnosis results. The fault diagnosis based on the single sensor has inherent uncertainty due to the influence of a fault characteristic model and other factors, and simply performing binary judgment on various faults according to an alarm threshold value can generate high false alarm rate and false miss rate.
Disclosure of Invention
The invention aims to: in order to overcome the defects in the prior art, the invention provides a motor health assessment method, a motor health assessment device and a storage medium based on a singlechip, and the health grade assessment of a motor is carried out by acquiring each operation parameter of the motor so as to identify four main faults, namely a rotor broken bar fault, a stator winding fault, a bearing fault and a motor shaft fault, so that the timely prediction and positioning of the motor faults are realized, and the guarantee is provided for the efficient and safe operation of the motor.
The technical scheme is as follows: in order to achieve the above purpose, the motor health assessment method based on the singlechip comprises the following steps:
step one: monitoring the working state of the motor during operation by using a sensor to acquire various operation parameters of the motor;
step two: preprocessing and feature extraction are carried out on the acquired operation parameters of the motor, and interference influence is eliminated so as to extract fault feature points therefrom;
step three: trust value determination is respectively carried out on single sensor alarms in certain faults so as to obtain a sensor monitoring value with high trust degree;
step four: carrying out regularized fusion with an expert knowledge base and carrying out weight distribution on fault types;
step five: dividing the health degree grade aiming at the motor, and carrying out health degree grade assessment on the motor according to the weight distribution of each fault;
step six: and carrying out fault prediction sequencing on each fault of the motor according to the type weight so as to predict and locate fault types and fault points.
Further, in the first step, the sensor signal transmission is connected with the corresponding singlechip, and the sensor comprises a current sensor for monitoring the current of the motor, a vibration sensor for monitoring the vibration of the motor, a temperature sensor for monitoring the temperature of the motor and the environment, and a rotation speed sensor for monitoring the rotation speed of the motor.
Further, in the second step, the preprocessing includes removing the rotation speed and the environmental interference on the temperature, and further includes denoising the measured motor current and vibration by adopting a wavelet packet method to remove the noise interference.
Further, in the third step, based on the false alarm probability and the false alarm probability, a bayesian function is adopted to calculate the trust value of the monitoring value of the sensor, so that the trust degree of the monitoring value of the sensor is determined, and the monitoring value of the sensor with high trust degree is reserved.
Further, in step four, the motor fault types include a rotor bar fault, a stator winding fault, a bearing fault, and a motor shaft fault, a rotor bar fault weight is assigned corresponding to the rotor bar fault, a stator winding weight is assigned corresponding to the stator winding fault, a bearing weight is assigned corresponding to the bearing fault, and a differential weight between a motor shaft temperature and an ambient temperature is assigned corresponding to the motor shaft fault.
Further, in the fifth step, the grade-differentiated weight values are classified according to the health grade of the motor, and various faults of the motor are classified into the corresponding health grade according to the correspondingly-allocated fault class weights.
Further, in step six, fault sequencing is performed according to type weights for the rotor bar breaking faults, stator winding faults, bearing faults and motor shaft faults, and fault types and fault positioning of the types are judged according to the maximum membership rule.
The device comprises a singlechip which is connected with each sensor for monitoring the operation parameters of the gate, and is provided with a processing module, and the processing module is used for carrying out data processing on each monitoring value obtained by the sensor so as to realize the motor health assessment method based on the singlechip.
The storage medium stores an executable program, and the executable program is executed by the processor to realize a motor health assessment method based on the singlechip.
The beneficial effects are that: the invention can predict and find early fault types and positioning fault points of the motor in time, improves monitoring quality and efficiency, gives an alarm when detecting abnormal motor state, effectively avoids the expansion of faults and improves the operation safety and production efficiency of the motor; in addition, the access network can upload information such as state parameters, health degree, abnormal alarm, prediction results, fault types, fault points and the like to the cloud server in real time, and data support is provided for remote monitoring and big data analysis.
Drawings
FIG. 1 is a schematic diagram of a motor health assessment principle;
fig. 2 is a schematic diagram of a fusion structure.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
As shown in fig. 1 and fig. 2, a motor health evaluation method based on a single chip microcomputer comprises the following steps:
step one: and monitoring the working state of the motor during operation by using the sensor to acquire various operation parameters of the motor.
In the first step, the sensor signal transmission is connected with the corresponding singlechip, and the sensor comprises a current sensor for monitoring the current of the motor, a vibration sensor for monitoring the vibration of the motor, a temperature sensor for monitoring the temperature of the motor and the environment and a rotation speed sensor for monitoring the rotation speed of the motor.
More specifically, 1 current sensor is installed at the three-phase power inlet wire of the motor; 1 vibration sensor is respectively arranged at the radial 0-degree angle and the 180-degree angle of the upper bearing of the motor; 1 vibration sensor is respectively arranged at the radial 0-degree angle and the 180-degree angle of the lower bearing of the motor; the motor is axially provided with 1 vibration sensor; 1 temperature sensor is respectively arranged on the motor stator and the bearing; the working environment is provided with 1 temperature sensor; and 1 rotation speed sensor is axially installed.
Step two: and preprocessing and extracting the characteristics of the acquired operation parameters of the motor, and eliminating interference influence to extract fault characteristic points therefrom.
In the second step, the preprocessing comprises the steps of removing the rotating speed and the environmental interference on the temperature, and further comprises the step of carrying out noise elimination processing on the measured motor current and vibration by adopting a wavelet packet method so as to eliminate the noise interference.
The feature extraction is as follows:
a: and (3) extracting characteristics of rotor broken bars: when the motor rotor breaks, an additional current component with characteristic frequency appears on the stator current, namely at the power frequency f 0 In the vicinity, a (1.+ -.2 s) f appears 0 (s is slip).
B: when the motor has an air gap eccentricity, an additional current component f of characteristic frequency appears on the stator current ecc I.e.Wherein f 0 For the power frequency, k is an integer (1, 2,3 … n), s is slip, and p is the pole pair number of the motor.
C: when there is a bearing failure in the motor, an additional current component of characteristic frequency will appear on the stator current, i.e. f=f 0 ±kf r Wherein f 0 For the power supply frequency, k is an integer (1, 2,3 … n), f r Is the spindle frequency.
D: when the motor has a turn-to-turn short circuit, an additional current component of characteristic frequency, namely f= (n+/-2 k (1-s)) f, appears on the stator current o Wherein f 0 The power supply frequency is n and k are integers (1, 2,3 and … n are taken), and s is slip.
E: when the three-phase magnetic field of the stator of the motor is asymmetric, a stator coil or an iron core is loosened, and an anchor bolt is loosened, the motor can generate 2f 0 In particular when the stator coil or core is loose, 4f may also occur 0 、6f 0 、8f 0 Is a harmonic of (a).
F: the motor is caused to vibrate when the motor rotor windings fail, and this vibration increases with increasing motor load, at f 0 Is + -2 s f on both sides of (a) 0 (s is slip).
G: when the motor air gap is uneven and eccentric, the generation period is 1/2s f 0 Is accelerated as the load increases.
H: when the bearing is poorly lubricated, the axial characteristic vibration is k f r Or f r K, where k is an integer, f r Is the spindle frequency.
I, a step of I; when the bearing is eccentric, the axial characteristic vibration is k f r Where k is an integer, f r Is the spindle frequency.
J; the axial characteristic vibration is f when the size of the bearing body is uneven c And k f r Where k is an integer, f c Is the cage frequency.
K: when the shaft is bent, the axial characteristic vibration is k f c ±f r Where k is an integer, f c For cage frequency, f r Is the spindle frequency.
L: characteristic value of temperature: when the temperature of the self-cooling motor is increased by 10 ℃, the difference between the motor shaft temperature and the ambient temperature is increased by 1.5-3 ℃. When the motor is jammed by a fault, an air duct or a load suddenly increases, the difference between the motor shaft temperature and the ambient temperature increases suddenly.
Step three: and respectively determining the trust value of the single sensor alarm in the case of a certain fault so as to obtain the sensor monitoring value with high trust degree.
In the third step, based on the false alarm probability and the false alarm probability, a Bayesian function is adopted to calculate the trust value of the monitoring value of the sensor, so that the trust degree of the monitoring value of the sensor is determined, and the monitoring value of the sensor with high trust degree is reserved.
The formula for determining the trust degree of the Bayesian function is as follows:
wherein: p (P) F Is the false alarm probability, P M Is the probability of missing report.
Step four: and (5) regularized fusion is carried out with an expert knowledge base, and weight distribution is carried out on fault types.
In the fourth step, the motor fault types include a rotor bar fault, a stator winding fault, a bearing fault and a motor shaft fault, a rotor bar breaking weight is allocated corresponding to the rotor bar fault, a stator winding weight is allocated corresponding to the stator winding fault, a bearing weight is allocated corresponding to the bearing fault, and a motor shaft temperature and an ambient temperature difference weight is allocated corresponding to the motor shaft fault.
The method specifically comprises the following steps: the weight of the broken rotor bar is 0.1, wherein: the weight A is 0.04, the weight E is 0.04, and the weight L is 0.01;
the stator winding weight is 0.3, wherein: d weight 0.15, E weight 0.04, F weight 0.09, L weight 0.02;
the bearing weight is 0.4, wherein: b weight 0.05, C weight 0.1, G weight 0.02, H weight 0.02, I weight 0.1, J weight 0.05, K weight 0.05, L weight 0.01;
the weight of the difference between the motor shaft temperature and the ambient temperature is 0.2, wherein: a weight is 0.02, B weight is 0.01, C weight is 0.04, D weight is 0.05, E weight is 0.02, F weight is 0.01, G weight is 0.01, H weight is 0.01, I weight is 0.01, J weight is 0.01, K weight is 0.01.
Step five: and dividing the health degree grade aiming at the motor, and carrying out health degree grade assessment on the motor according to the weight distribution of each fault.
In the fifth step, the grade-differentiated weight values are classified according to the health grade of the motor, and various faults of the motor are classified into the corresponding health grade according to the correspondingly-distributed fault class weights.
Health grade assessment: < 0.5 healthy; 0.5-0.7 mild; 0.7-0.9 medium; > 0.9 severe.
Step six: and carrying out fault prediction sequencing on each fault of the motor according to the type weight so as to predict and locate fault types and fault points.
In the sixth step, aiming at the rotor broken bar fault, the stator winding fault, the bearing fault and the motor shaft fault, the fault is ordered according to type weights, and the fault type and the type fault positioning are judged according to the maximum membership rule.
Of course, besides the four main fault types, the motor has other faults, and the other faults can be subjected to weight distribution so as to participate in the health grade evaluation of the motor.
The device comprises a singlechip which is connected with each sensor for monitoring the operation parameters of the gate, and is provided with a processing module, and the processing module is used for carrying out data processing on each monitoring value obtained by the sensor so as to realize the motor health assessment method based on the singlechip.
The storage medium stores an executable program, and the executable program is executed by the processor to realize a motor health assessment method based on the singlechip.
The invention has the following advantages:
(1) Early fault types and positioning fault points of the motor can be predicted and found in time, monitoring quality and efficiency are improved, an alarm can be sent when abnormal motor states are detected, the expansion of faults is effectively avoided, and operation safety and production efficiency of the motor are improved;
(2) The cloud server can be accessed to a network to upload information such as state parameters, health degree, abnormal alarm, prediction results, fault types, fault points and the like to the cloud server in real time, and data support is provided for remote monitoring and big data analysis.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (9)
1. A motor health assessment method based on a singlechip is characterized by comprising the following steps of: the method comprises the following steps:
step one: monitoring the working state of the motor during operation by using a sensor to acquire various operation parameters of the motor;
step two: preprocessing and feature extraction are carried out on the acquired operation parameters of the motor, and interference influence is eliminated so as to extract fault feature points therefrom;
step three: trust value determination is respectively carried out on single sensor alarms in certain faults so as to obtain a sensor monitoring value with high trust degree;
step four: carrying out regularized fusion with an expert knowledge base and carrying out weight distribution on fault types;
step five: dividing the health degree grade aiming at the motor, and carrying out health degree grade assessment on the motor according to the weight distribution of each fault;
step six: and carrying out fault prediction sequencing on each fault of the motor according to the type weight so as to predict and locate fault types and fault points.
2. The motor health assessment method based on the single-chip microcomputer according to claim 1, wherein the motor health assessment method is characterized in that: in the first step, the sensor signal transmission is connected with the corresponding singlechip, and the sensor comprises a current sensor for monitoring the current of the motor, a vibration sensor for monitoring the vibration of the motor, a temperature sensor for monitoring the temperature of the motor and the environment and a rotation speed sensor for monitoring the rotation speed of the motor.
3. The motor health assessment method based on the single-chip microcomputer as set forth in claim 2, wherein: in the second step, the preprocessing comprises the steps of removing the rotating speed and the environmental interference on the temperature, and further comprises the step of carrying out noise elimination processing on the measured motor current and vibration by adopting a wavelet packet method so as to eliminate the noise interference.
4. The motor health assessment method based on the single-chip microcomputer as set forth in claim 2, wherein: in the third step, based on the false alarm probability and the false alarm probability, a Bayesian function is adopted to calculate the trust value of the monitoring value of the sensor, so that the trust degree of the monitoring value of the sensor is determined, and the monitoring value of the sensor with high trust degree is reserved.
5. The motor health assessment method based on the single-chip microcomputer according to claim 4, wherein the motor health assessment method is characterized in that: in the fourth step, the motor fault types include a rotor bar fault, a stator winding fault, a bearing fault and a motor shaft fault, a rotor bar breaking weight is allocated corresponding to the rotor bar fault, a stator winding weight is allocated corresponding to the stator winding fault, a bearing weight is allocated corresponding to the bearing fault, and a motor shaft temperature and an ambient temperature difference weight is allocated corresponding to the motor shaft fault.
6. The motor health assessment method based on the single-chip microcomputer according to claim 5, wherein the motor health assessment method is characterized in that: in the fifth step, the grade-differentiated weight values are classified according to the health grade of the motor, and various faults of the motor are classified into the corresponding health grade according to the correspondingly-distributed fault class weights.
7. The motor health assessment method based on the single-chip microcomputer according to claim 6, wherein the motor health assessment method is characterized in that: in the sixth step, aiming at the rotor broken bar fault, the stator winding fault, the bearing fault and the motor shaft fault, the fault is ordered according to type weights, and the fault type and the type fault positioning are judged according to the maximum membership rule.
8. The device comprises a singlechip which is connected with each sensor for monitoring the operation parameters of the gate, wherein the singlechip is provided with a processing module, and the processing module is used for processing data of each monitoring value obtained by the sensor to realize the motor health assessment method based on the singlechip according to any one of claims 1-7.
9. A storage medium, characterized in that: an executable program is stored in the motor health evaluation device, and the executable program is executed by a processor to realize the motor health evaluation method based on the singlechip according to any one of claims 1 to 7.
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