Electric motor health monitoring and abnormity diagnostic method based on feature selection and mahalanobis distance
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A Mahalanobis distance and feature selection technology, applied in the direction of motor generator testing, etc., can solve difficult problems such as motor health monitoring and abnormal diagnosis
What is AI technical title?
AI technical title is built by PatSnap AI team. It summarizes the technical point description of the patent document.
A Mahalanobis distance and feature selection technology, applied in the direction of motor generator testing, etc., can solve difficult problems such as motor health monitoring and abnormal diagnosis
CN103995229AActive 📅 Publication Date: 2014-08-20HUAWEI TEHCHNOLOGIES CO LTD
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[0026] The present invention will be further described below in conjunction with the accompanying drawings.
[0027] refer to figure 1 , a motor health monitoring and abnormal diagnosis method based on feature selection and Mahalanobis distance, including the following steps:
[0028] Step 1: Collect vibration, current and rotational speed signals from the motor under normal working conditions and the test motor.
[0029] Step 2: Calculate the characteristics of the collected motor signals under normal working conditions to obtain the feature space, specifically: first, calculate the time-domain characteristics of the vibration signal, including effective value, maximum peak value, peak-to-peak value, kurtosis, average Value, variance, standard deviation, skewness, crest factor, and power. Then, calculate the effective value of the current, and construct the feature space S together with the motor speed;
[0030] The feature space includes the effective value, maximum peak ...
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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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Claims
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