Motor fault diagnosis method based on RBF and PCA-SVDD

Abstract

The invention discloses a motor fault diagnosis method based on RBF and PCA-SVDD, and the method comprises the steps: collecting historical data of motor operation parameters; arranging the historical data of motor operation parameters and forming a sample; designing the structure of a neural network according to the sample; carrying out the classification of the motor fault data through building a PCA-SVDD fault diagnosis model; and carrying out adjustment of a database according to real-time data in a motor operation process through scientific calculation. The method is high in operation adaptive capability, and can achieve the real-time detection of a plurality of types of motor faults.

Description

technical field [0001] The invention relates to a motor fault detection method, in particular to a motor fault diagnosis method based on RBF and PCA-SVDD. Background technique [0002] Electric motor is a kind of equipment widely used in industrial production. The operation status of electric motor is of great significance to enterprise production. The fault detection of electric motor has attracted more and more people's attention. [0003] Most of the traditional motor testing methods are only aimed at a single motor fault, the design is complex, the versatility is poor, and the testing process is cumbersome, which is not conducive to the integration of the test system. However, the motor current signal analysis method only analyzes specific one or two fault frequencies to determine whether the motor has a certain fault, and the detection is single, which has relatively large limitations. Moreover, the motor current signal analysis method needs to collect frequency, and t...

AI Technical Summary

Problems solved by technology

[0003] Most of the traditional motor testing methods are only aimed at a single motor fault, the design is complex, the versatility is poor, and the testing process is cumbersome, which is not conducive to the integration of the test system.

However, the motor current signal analysis method only analyzes one or two specific fault frequencies to judge whether there is a fault in the motor, and the detection is single, which has great limitations.

Moreover, the motor current signal analysis method needs to collect frequency, and the steps are cumbersome. When the system is disturbed, its detection system is easily affected by external changes. The reliability is high, the diagnostic reliability cannot be guaranteed, and the detection performance is poor

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