Electric motor health monitoring and abnormity diagnostic method based on feature selection and mahalanobis distance

Abstract

The invention provides an electric motor health monitoring and abnormity diagnostic method based on feature selection and the mahalanobis distance. The method includes the first step of conducting data acquisition on vibration signals, current signals and rotating speed signals of electric motors, conducting feature computing on the signals, constructing feature spaces and selecting feature vectors for calculation of the mahalanobis distance in a feature selection method, the second step of the mahalanobis distances of the electric motors in a normal operation state and constructing mahalanobis spaces indicating the normal operation state of the electric motors, and the third step of calculating the mahalanobis distances according to signals of tested electric motors with unknown health conditions with reference to statistic parameters of the motors in the normal operation state and judging the health condition of the tested electric motors through comparison of the mahalanobis spaces. Through the electric motor health monitoring and abnormity diagnostic method based on feature selection and the mahalanobis distance, since the signals of the electric motors in the normal operation state are used for constructing the mahalanobis spaces, health monitoring and abnormity diagnosis on the motors in an unknown operation state can be effectively achieved.

Description

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AI Technical Summary

Benefits of technology

This technology helps train new horses by analyzing their movements during operation without actually being driven over or underground. It uses signals from an engine's control system that work properly when they start up but doesn’t help them diagnose any issues with themselves.

Problems solved by technology

This technical problem addressed by this patented method relates to improving the accuracy and efficiency of detecting and diagnosing motors' failures during their operation. Current methods require extensive knowledge about how well they work with different types of machines but cannot accurately identify any specific type of failure based on its operating conditions alone.

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