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Vibration signal characteristic value selection method and elevator health state evaluation or fault diagnosis method

A vibration signal and eigenvalue technology, which is applied in the field of vibration signal eigenvalue selection, elevator health status evaluation or fault diagnosis, can solve problems such as the recursive process is too long, affects, and affects the accuracy of the diagnosis model of mechanical equipment operation status

Active Publication Date: 2021-02-19
SHANGHAI MITSUBISHI ELEVATOR CO LTD
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AI Technical Summary

Problems solved by technology

[0007] In order to obtain the best eigenvalue combination, the method of recursive feature elimination is usually used for eigenvalue selection, but its disadvantage is that when the number of eigenvalues ​​is too large, the calculation scale is huge, and the entire recursive process is too long, which affects the efficiency of feature selection; In addition, due to the randomness of the selection of the training data set and the test data set, the effect feature value deleted during the recursive process may be affected by the data distribution, thereby affecting the accuracy of the diagnostic model of the mechanical equipment operating state

Method used

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  • Vibration signal characteristic value selection method and elevator health state evaluation or fault diagnosis method
  • Vibration signal characteristic value selection method and elevator health state evaluation or fault diagnosis method
  • Vibration signal characteristic value selection method and elevator health state evaluation or fault diagnosis method

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Experimental program
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Effect test

Embodiment 1

[0083] Such as figure 2 As shown, the vibration signal eigenvalue selection method includes the following steps:

[0084] 1. The operating state of mechanical equipment includes one normal state and m-1 abnormal states, where m is an integer greater than 1;

[0085] Perform preprocessing on the original vibration signal, so that there are n vibration signal data in each mechanical equipment operating state, and delete the redundant original vibration signal, where n is an integer greater than 1;

[0086] 2. Perform signal processing on each group of preprocessed vibration signal data, and extract b types of eigenvalues, b is an integer greater than 2; m×n groups of preprocessed vibration signal data are extracted to obtain m×n×b eigenvalues ;

[0087] 3. Constructing an eigenvalue matrix X and a mechanical equipment state label vector Y by the m×n×b eigenvalues;

[0088] The eigenvalue matrix X has m×n rows and b columns;

[0089] Mechanical equipment state label vector Y...

Embodiment 2

[0118] Based on the vibration signal eigenvalue selection method of Embodiment 1, in step 5, the comprehensive distance correlation coefficient of each time domain eigenvalue is by calculating the distance correlation between this kind of time domain eigenvalue and other p-1 kinds of time domain eigenvalues The sum of the coefficients, divided by p-1;

[0119] The comprehensive distance correlation coefficient of each frequency domain eigenvalue is calculated by calculating the sum of the distance correlation coefficients between this frequency domain eigenvalue and other q-1 frequency domain eigenvalues, and then dividing by q-1;

[0120] The comprehensive distance correlation coefficient of each time-frequency domain eigenvalue is calculated by calculating the sum of the distance correlation coefficients between this time-frequency domain eigenvalue and other r-1 time-frequency domain eigenvalues, and then dividing by r-1;

[0121]

[0122] SdCor(Ax) is the comprehensive ...

Embodiment 3

[0124] Based on the vibration signal eigenvalue selection method of Embodiment 1, in step 5, the time domain, frequency domain, and time-frequency domain eigenvalue correlation analysis are carried out, and the eigenvalues ​​are subjected to a second rough screening, which is based on the calculated time domain, frequency Domain and time-frequency domain eigenvalue comprehensive distance correlation coefficient, according to the set ratio, delete some eigenvalues ​​with larger comprehensive distance correlation coefficients in time domain, frequency domain and time-frequency domain, and get the second time after eigenvalue correlation analysis Time-domain eigenvalues, frequency-domain eigenvalues ​​and frequency-domain eigenvalues ​​after coarse screening.

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Abstract

The invention discloses a vibration signal characteristic value selection method, which comprises the following steps: performing variance analysis on characteristic values of m * n groups of vibration signals, analyzing the relevance between the characteristic values and label values of vibration signal state labels, and performing primary coarse screening by deleting the characteristic values which are in low relevance with the label values; calculating a distance correlation coefficient of each characteristic value and other characteristic values to obtain a comprehensive distance correlation coefficient, performing characteristic value correlation analysis, and performing secondary coarse screening by deleting the characteristic value with the high comprehensive distance correlation coefficient; and performing eigenvalue matrix combination on the eigenvalues reserved by the second coarse screening, and performing recursive feature elimination based on the combined eigenvalue matrixto obtain an optimal eigenvalue combination. According to the vibration signal characteristic value selection method, the characteristic values of the vibration signal data can be effectively screened, the optimal characteristic value combination forming the mechanical equipment operation state vibration model is obtained, the dimensionality of the model is effectively reduced, calculation resources are saved, and the accuracy of the model is improved.

Description

technical field [0001] The invention relates to the technical field of machine learning and data mining, in particular to a method for selecting vibration signal eigenvalues ​​and an elevator health status assessment or fault diagnosis method. Background technique [0002] In recent years, with the rapid development of sensor technology, miniaturized and low-cost vibration sensors are used to obtain vibration signals during the operation of mechanical equipment by installing them on mechanical equipment, such as figure 1 shown. A large number of normal, abnormal and faulty vibration signals of mechanical equipment obtained by sensors are used to extract the characteristic values ​​of the corresponding vibration signals after data cleaning and signal processing. Based on machine learning technology and data training, a diagnostic model of mechanical equipment operating status is established. According to the above-mentioned diagnosis model of mechanical equipment operation s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G01H1/00G01M13/00G01M13/045B66B5/00
CPCG01H1/00G01M13/00G01M13/045B66B5/0037B66B5/0018G06F2218/08G06F2218/12G06F18/24323G06F18/214
Inventor 俞英杰郑斌周俊帆马骧越
Owner SHANGHAI MITSUBISHI ELEVATOR CO LTD
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