Fault diagnosis method by combining correlation analysis and data fusion

Abstract

The invention discloses a fault diagnosis method by combining correlation analysis and data fusion, and is used for solving the technical problem of low fault diagnosis result precision of the existing fault diagnosis method. The fault diagnosis method has the technical scheme that equipment information from different measuring sources is extracted; multi-source information and same-source information corresponding to the existing fault mode are subjected to correlation analysis; the obtained correlation degree is used as a correlation coefficient between the existing fault mode and the multi-source data measured in a fault to be diagnosed; the information of a plurality of sources is synthesized by a data fusion method; the reliability, belonging to the existing fault mode, of the fault to be diagnosed is calculated; the fault mode corresponding to the maximum reliability is selected from the reliability. The method of replacing the correlation coefficient in the multi-source data fusion process by the grey correlation degree between the single physical quantity in the process of the fault to be diagnosed and the same physical quantity in the existing fault type is adopted, so that the single physical quantity and the multi-information fusion processes are effectively linked, and the precision of the fault diagnosis result is improved.

Description

technical field [0001] The invention relates to a fault diagnosis method, in particular to a fault diagnosis method combining correlation analysis and data fusion. Background technique [0002] The document "Motor Fault Diagnosis Based on Neural Network and D-S Evidence Theory, Proceedings of the Fifth National Vibration Utilization Engineering Conference and the Fourth National Symposium on Ultrasonic Motor Technology, 2012" discloses a fault diagnosis method. This method uses multiple sensors to test the diagnosed object, and obtains the membership degree value of each sensor for various faults under a certain symptom; the fault membership value vector of all sensors is used as the input of the neural network, and the network output is the fusion value. Symptoms belong to the membership value vectors of various faults; finally, the fault decision is made using the rule-based judgment principle. The method described in the literature uses the membership degree obtained by ...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the deficiency of low accuracy of fault diagnosis results of existing fault diagnosis methods, the present invention provides a fault diagnosis method combining correlation analysis and data fusion

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