CN113609964A - Motor abnormal vibration early warning method and system - Google Patents

Motor abnormal vibration early warning method and system Download PDF

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CN113609964A
CN113609964A CN202110886276.4A CN202110886276A CN113609964A CN 113609964 A CN113609964 A CN 113609964A CN 202110886276 A CN202110886276 A CN 202110886276A CN 113609964 A CN113609964 A CN 113609964A
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motor
sample
load interval
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load
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梁朋
唐丽
赵忠
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Xi'an Shuanghe Software Engineering Co ltd
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    • G06F2218/12—Classification; Matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/34—Testing dynamo-electric machines
    • G01R31/343—Testing dynamo-electric machines in operation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
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    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F2218/08—Feature extraction

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Abstract

The invention provides a motor abnormal vibration early warning method and system, wherein the method comprises the following steps: collecting vibration data and electrical data of the motor, obtaining a load interval of the motor according to the electrical data of the motor, and constructing a reference eigenvector matrix X of the vibration data of the motor in different load intervalsm×nWherein m is the number of statistical samples, and n is the characteristic dimension of each sample data; according to the reference eigenvector matrix X of each load intervalm×nRespectively calculating monitoring threshold values in each load interval; according to the sample to be detected and the reference eigenvector matrix Xm×nAnd judging whether the motor is abnormal or not according to the Markov distance index and the monitoring threshold value of the current load interval. The motor of the inventionThe constant vibration early warning method has the advantage of accurate monitoring result.

Description

Motor abnormal vibration early warning method and system
Technical Field
The invention relates to the field of motor monitoring, in particular to a motor abnormal vibration early warning method and system.
Background
The main mechanical parts of the motor comprise a rotor, a stator, a bearing and the like, and the motor has vibration characteristics under different conditions due to the service life of a unit and load change. Vibration is an important aspect of the operating characteristics of an electric machine.
During the operation of the motor, the vibration condition of the motor changes due to the damage or abnormality of each component, and the amplitude of the vibration change is often not obvious in the early stage of the damage and is difficult to detect. Finally, irreversible damage to the critical components can be caused, which can lead to long-term shutdown of the unit and serious economic loss.
The traditional state monitoring only monitors a single characteristic quantity, and judges the running state of equipment by a method of artificially setting a threshold value. This method requires a great deal of manual intervention and is experience-dependent. And a single variable often does not reflect the operating state of the device in its entirety. Meanwhile, the load condition of the equipment is not considered when the vibration condition of the equipment is considered, and the load condition of the equipment generally changes in real time due to different processes in an industrial field. Also, the different load conditions may cause the different vibration conditions of the device. Therefore, if the load state of the equipment is not considered, only the vibration index is monitored, and the monitoring result may be deviated.
Disclosure of Invention
The invention aims to provide a motor abnormal vibration early warning method and system with accurate monitoring results.
The embodiment of the invention provides an early warning method for abnormal vibration of a motor, which comprises the following steps:
collecting vibration data and electrical data of the motor, obtaining a load interval of the motor according to the electrical data of the motor, and constructing a reference eigenvector matrix x of the vibration data of the motor in different load intervalsm×nWherein m is the number of statistical samples, and n is the characteristic dimension of each sample data;
according to the reference eigenvector matrix X of each load intervalm×nRespectively calculating monitoring threshold values in each load interval;
calculating the current load interval of the sample to be measured and the matrix X of the sample to be measured and the reference eigenvector in the current load intervalm×nIn the Ma's system ofA distance index;
according to the sample to be measured and the reference eigenvector matrix xm×nAnd judging whether the motor is abnormal or not according to the Markov distance index and the monitoring threshold value of the current load interval.
In the embodiment of the invention, the reference characteristic vector matrix X is obtained according to each load intervalm×nRespectively calculating the monitoring threshold value in each load interval, wherein the monitoring threshold value comprises the following steps:
first, a reference feature vector matrix X is formedm×nNormalizing to obtain a normalized eigenvector matrix Z;
then, the samples are calculated by the mahalanobis distance
Figure BDA0003194236520000021
Wherein C represents a covariance matrix of matrix Z;
finally, the monitoring threshold value MD is calculated through the 3Sigma criterionthreshold＝MDtrain×3。
In the embodiment of the invention, the current load interval of the sample to be detected and the matrix x of the characteristic vector of the sample to be detected and the reference in the current load interval are calculatedm×nThe mahalanobis distance index of (1) includes:
calculating the load rate of the sample, and obtaining the load interval where the sample is located according to the load rate of the sample;
extracting characteristic vector y ═ y1, y2, …, yN from the sample to be tested]And carrying out normalization processing to calculate a reference characteristic vector matrix X between the current load interval and the normalized characteristic vector matrix Xm×nMahalanobis distance index of the corresponding normalized feature vector matrix Z: MD ═ yC- 1yT。
In the embodiment of the present invention, obtaining the load section of the motor according to the electrical data of the motor includes:
calculating the load rate of the motor according to the electrical data of the motor;
obtaining a load interval where the sample is located according to the load rate of the sample, wherein the load interval is divided according to the following rules:
the load rate is from 30% to 100%, and the load interval is selected in a left-closed and right-opened mode according to the principle that 5 percent of load interval is adopted.
In the embodiment of the invention, the method comprises the following steps of according to a sample to be detected and a reference eigenvector matrix Xm×nJudging whether the motor is abnormal by the Mahalanobis distance index, comprising the following steps:
the collected sample to be detected and a reference characteristic vector matrix X are combinedm×nComparing the Markov distance index with the monitoring threshold value of the corresponding load interval, and when the Markov distance indexes of three continuously collected samples to be measured in the same load interval and the reference characteristic vector matrix exceed the monitoring threshold value, determining that the motor vibration state is abnormal and sending an alarm.
In the embodiment of the invention, the vibration data of the motor comprises vibration data of a driving end and a non-driving end of the motor.
In the embodiment of the invention, the electric data of the motor comprises three-phase voltage and current data.
In an embodiment of the present invention, there is also provided an early warning system for abnormal vibration of a motor, including:
the acquisition module is used for acquiring vibration data and electrical data of the motor;
the reference characteristic vector matrix construction module is used for obtaining a load interval of the motor according to the electrical data of the motor and constructing a reference characteristic vector matrix X of vibration data of the motor in different load intervalsm×nWherein m is the number of statistical samples, and n is the characteristic dimension of each sample data;
a monitoring threshold calculation module for calculating a reference eigenvector matrix X according to each load intervalm×nRespectively calculating monitoring threshold values in each load interval;
a Mahalanobis distance index calculation module for calculating the current load interval of the sample to be measured and the matrix X of the characteristic vector of the sample to be measured and the reference in the current load intervalm×nThe mahalanobis distance index of (1);
a motor state judgment module for judging the state of the motor according to the sample to be detected and the reference eigenvector matrix Xm×nAnd judging whether the motor is abnormal or not according to the Markov distance index and the monitoring threshold value of the current load interval.
In the embodiment of the invention, the acquisition module comprises vibration acceleration sensors which are respectively arranged at the driving end and the non-driving end of the motor and are respectively used for monitoring vibration signals of the driving end and the non-driving end of the motor.
In the embodiment of the invention, the acquisition module further comprises a voltage transformer and a current transformer which are respectively used for acquiring three-phase voltage and three-phase current data of the motor.
Compared with the prior art, in the technical scheme of the invention, the reference characteristic vector matrix X is obtained according to each load intervalm×nRespectively calculating the monitoring threshold value in each load interval according to the sample to be measured and the reference eigenvector matrix Xm×nThe Mahalanobis distance index and the monitoring threshold value of the current load interval are used for judging whether the motor is abnormal or not, the running state of the equipment is monitored through multidimensional characteristics in statistical process control, the monitoring index is built in a self-adaptive mode, the threshold value is calculated automatically, the load state of the equipment in the running process is considered, manual operation is simplified, the multidimensional characteristics of signals are applied, the defect that the traditional state monitoring threshold value is specific can be avoided, and the accuracy of the model is improved.
Drawings
Fig. 1 is a flowchart of a method for warning abnormal vibration of a motor according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an early warning system for abnormal vibration of a motor according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following describes the implementation of the present invention in detail with reference to specific embodiments.
As shown in fig. 1, in the embodiment of the present invention, a method for warning abnormal vibration of a motor is provided, which includes steps S1-S5. The following description will be made separately.
Step S1: and (6) data acquisition.
It should be noted that before determining whether the motor vibration is abnormal, vibration data of the motor in normal operation needs to be collected, and vibration data features of the motor in normal operation need to be extracted from the vibration data of the motor in normal operation. Therefore, in this step, electrical data and vibration data of the motor under various load conditions are collected.
Specifically, a vibration signal monitoring point is respectively arranged at the driving end and the non-driving end of the motor and is respectively used for monitoring the operation conditions of the driving end and the non-driving end of the motor. The vibration sensor adopts a vibration acceleration sensor, and the sampling frequency of the sensors at two measuring points is set above 20 kHz. A voltage transformer and a current transformer are adopted to collect three-phase voltage and current signals input by a motor, and the sampling frequency is set to be 5 k-15 kHz.
Step S2: and constructing a feature matrix.
In this step, first, the load factor of the motor is calculated from the electrical data of the motor;
obtaining a load interval where the sample is located according to the load rate of the sample, wherein the load interval is divided according to the following rules:
the load rate is from 30% to 100%, and the load interval is selected in a left-closed and right-opened mode according to the principle that 5 percent of load interval is adopted.
After the load intervals of the motor are obtained, respectively constructing a reference eigenvector matrix X of vibration data of the motor in different load intervalsm×nWherein m is the number of statistical samples, and n is the characteristic dimension of each sample data.
Step S3: and calculating the monitoring threshold values of the motor in different load intervals.
It should be noted that, in different load intervals, the monitoring thresholds of the motor vibration are different, and the specific calculation manner is as follows:
first, a reference feature vector matrix X is formedm×nNormalizing to obtain a normalized eigenvector matrix Z;
then, the samples are calculated by the mahalanobis distance
Figure BDA0003194236520000051
Wherein C represents a covariance matrix of matrix Z;
finally, the monitoring threshold value MD is calculated through the 3Sigma criterionthreshold＝MDtrain×3。
Step S4: calculating the current load interval of the sample to be measured and the matrix x of the sample to be measured and the reference eigenvector in the current load intervalm×nThe mahalanobis distance index.
The specific process comprises the following steps:
firstly, calculating the load rate of a sample, and obtaining a load interval where the sample is located according to the load rate of the sample;
then, extracting the characteristic vector y ═ y1, y2, …, yN from the sample to be tested]And carrying out normalization processing to calculate a reference characteristic vector matrix X between the current load interval and the normalized characteristic vector matrix Xm×nMahalanobis distance index of the corresponding normalized feature vector matrix Z: MID ═ yC-1yT。
Step S5: and judging whether the motor is abnormal or not.
Specifically, the sample to be measured obtained by calculation in step S4 and the reference feature vector matrix Xm×nThe mahalanobis distance index is compared with the monitoring threshold value of the corresponding load interval, and when the mahalanobis distance indexes of three continuously collected samples to be detected in the same load interval and the reference characteristic vector matrix exceed the monitoring threshold value, the motor vibration state is determined to be abnormal, and the condition of misjudgment is avoided. And sending an alarm after judging that the vibration state of the motor is abnormal.
As shown in fig. 2, in the embodiment of the present invention, an early warning system for abnormal vibration of a motor is further provided, which includes an acquisition module 1, a reference feature vector matrix construction module 2, a monitoring threshold calculation module 3, a mahalanobis distance index calculation module 4, and a motor state judgment module 5.
The acquisition module 1 is used for acquiring vibration data and electrical data of the motor. In the embodiment of the invention, the acquisition module 1 comprises vibration acceleration sensors respectively arranged at the driving end and the non-driving end of the motor and is respectively used for monitoring vibration signals of the driving end and the non-driving end of the motor. The acquisition module further comprises a voltage transformer and a current transformer which are respectively used for acquiring three-phase voltage and three-phase current data of the motor.
The reference eigenvector matrix construction module 2 is used for obtaining the load interval of the motor according to the electrical data of the motor and constructing the reference eigenvector matrix X of the vibration data of the motor in different load intervalsm×nWherein m is the number of statistical samples, and n is the characteristic dimension of each sample data.
The monitoring threshold calculation module 3 is configured to calculate a reference eigenvector matrix X according to each load intervalm×nAnd respectively calculating the monitoring threshold value in each load interval.
The mahalanobis distance index calculation module 4 is used for calculating the current load interval of the sample to be measured and the matrix X of the characteristic vector of the sample to be measured and the reference in the current load intervalm×nThe mahalanobis distance index.
The motor state judgment module 5 is used for judging whether the motor state is consistent with the reference characteristic vector matrix X or not according to the sample to be detectedm×nAnd judging whether the motor is abnormal or not according to the Markov distance index and the monitoring threshold value of the current load interval. And when the Markov distance indexes of three continuously collected samples to be detected in the same load interval and the reference characteristic vector matrix exceed the monitoring threshold, determining that the vibration state of the motor is abnormal, and giving an alarm.
In summary, in the technical solution of the present invention, the reference eigenvector matrix X is obtained according to each load intervalm×nRespectively calculating the monitoring threshold value in each load interval according to the sample to be measured and the reference eigenvector matrix Xm×nThe Mahalanobis distance index and the monitoring threshold value of the current load interval are used for judging whether the motor is abnormal or not, the running state of the equipment is monitored through multidimensional characteristics in statistical process control, the monitoring index is built in a self-adaptive mode, the threshold value is calculated automatically, the load state of the equipment in the running process is considered, manual operation is simplified, the multidimensional characteristics of signals are applied, the defect that the traditional state monitoring threshold value is specific can be avoided, and the accuracy of the model is improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (10)

1. An early warning method for abnormal vibration of a motor is characterized by comprising the following steps:
collecting vibration data and electrical data of the motor, obtaining a load interval of the motor according to the electrical data of the motor, and constructing a reference eigenvector matrix X of the vibration data of the motor in different load intervalsm×nWherein m is the number of statistical samples, and n is the characteristic dimension of each sample data;
according to the reference eigenvector matrix X of each load intervalm×nRespectively calculating monitoring threshold values in each load interval;
calculating the current load interval of the sample to be measured and the matrix X of the sample to be measured and the reference eigenvector in the current load intervalm×nThe mahalanobis distance index of (1);
according to the sample to be detected and the reference eigenvector matrix Xm×nAnd judging whether the motor is abnormal or not according to the Markov distance index and the monitoring threshold value of the current load interval.
2. The early warning method of abnormal vibration of a motor according to claim 1, wherein the matrix X of the reference eigenvector for each load section is used as a basism×nRespectively calculating the monitoring threshold value in each load interval, wherein the monitoring threshold value comprises the following steps:
first, a reference feature vector matrix X is formedm×nNormalizing to obtain a normalized eigenvector matrix Z;
then, the samples are calculated by the mahalanobis distance
Figure FDA0003194236510000011
Wherein C represents a covariance matrix of matrix Z;
finally, the monitoring threshold value MD is calculated through the 3Sigma criterionthreshold＝MDtrain×3。
3. The method of claim 2, wherein the current load interval of the sample to be measured and the matrix X of the sample to be measured and the reference eigenvector in the current load interval are calculatedm×nThe mahalanobis distance index of (1) includes:
calculating the load rate of the sample, and obtaining the load interval where the sample is located according to the load rate of the sample;
extracting characteristic vector y ═ y1, y2, …, yN from the sample to be tested]And carrying out normalization processing to calculate a reference characteristic vector matrix X between the current load interval and the normalized characteristic vector matrix Xm×nMahalanobis distance index of the corresponding normalized feature vector matrix Z: MD ═ yC-1yT。
4. The method of claim 1, wherein obtaining the load section of the motor according to the electrical data of the motor comprises:
calculating the load rate of the motor according to the electrical data of the motor;
obtaining a load interval where the sample is located according to the load rate of the sample, wherein the load interval is divided according to the following rules:
the load rate is from 30% to 100%, and the load interval is selected in a left-closed and right-opened mode according to the principle that 5 percent of load interval is adopted.
5. The method of claim 1, wherein the method is based on a sample to be measured and a reference eigenvector matrix Xm×nJudging whether the motor is abnormal by the Mahalanobis distance index, comprising the following steps:
the collected sample to be detected and a reference characteristic vector matrix X are combinedm×nComparing the Markov distance index with the monitoring threshold value of the corresponding load interval, and when the Markov distance indexes of three continuously collected samples to be measured in the same load interval and the reference characteristic vector matrix exceed the monitoring threshold value, determining that the motor vibration state is abnormal and sending an alarm.
6. The method of warning of abnormal vibration of a motor according to claim 1, wherein the vibration data of the motor includes vibration data of a driving side and a non-driving side of the motor.
7. The method of claim 1, wherein the electrical data of the motor comprises three-phase voltage and current data.
8. An early warning system of abnormal vibration of a motor, comprising:
the acquisition module is used for acquiring vibration data and electrical data of the motor;
the reference characteristic vector matrix construction module is used for obtaining a load interval of the motor according to the electrical data of the motor and constructing a reference characteristic vector matrix X of vibration data of the motor in different load intervalsm×nWherein m is the number of statistical samples, and n is the characteristic dimension of each sample data;
a monitoring threshold calculation module for calculating a reference eigenvector matrix X according to each load intervalm×nRespectively calculating monitoring threshold values in each load interval;
a Mahalanobis distance index calculation module for calculating the current load interval of the sample to be measured and the matrix X of the characteristic vector of the sample to be measured and the reference in the current load intervalm×nThe mahalanobis distance index of (1);
a motor state judgment module for judging the state of the motor according to the sample to be detected and the reference eigenvector matrix Xm×nAnd judging whether the motor is abnormal or not according to the Markov distance index and the monitoring threshold value of the current load interval.
9. The method for warning of abnormal vibration of a motor according to claim 8, wherein the collection module includes vibration acceleration sensors respectively disposed at a driving end and a non-driving end of the motor, and respectively configured to monitor vibration signals at the driving end and the non-driving end of the motor.
10. The method for early warning of abnormal vibration of a motor according to claim 9, wherein the collection module further comprises a voltage transformer and a current transformer, which are respectively used for acquiring three-phase voltage and three-phase current data of the motor.
CN202110886276.4A 2021-08-03 2021-08-03 Motor abnormal vibration early warning method and system Pending CN113609964A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114279555A (en) * 2021-12-27 2022-04-05 深圳市双合电气股份有限公司 Motor abnormal vibration monitoring method and system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114279555A (en) * 2021-12-27 2022-04-05 深圳市双合电气股份有限公司 Motor abnormal vibration monitoring method and system

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