Bearing health state identification method based on probabilistic neural network

Abstract

A bearing health state identification method based on the probabilistic neural network comprises the first step of collecting original signals; the second step of selecting and evaluating parameters, wherein the capability for allocating known data of the selected parameters is evaluated according to a feature set and the selected statistic features, and the feature set is generated by a vibration signal worked out based on the principle of statistics; the third step of constructing a probabilistic neural network model; the fourth step of inputting the input parameters to the probabilistic neural network to carry out state identification. According to the method, the operating states of a bearing comprise a normal state, a sub-health state and a failure state, the health states of the bearing are evaluated based on the PNN, a sample entropy and the like are used for serving as the input feature parameters of the PNN, the distribution capability of the data is evaluated, and the recognized result of the bearing health states is obtained by experimentally comparing a traditional normal-failure model of the bearing states.

Description

technical field [0001] The invention relates to a fault diagnosis method for rolling bearings of mechanical equipment in industrial production, in particular to a bearing health state identification method based on a probabilistic neural network. Background technique [0002] The fault diagnosis technology of mechanical equipment is becoming more and more important in current achievements. If the equipment failure is not discovered and eliminated in time, it will not only cause damage to the mechanical equipment, but also lead to a serious crash. The requirements for mechanical safety and reliability in industrial production are getting higher and higher, and intelligent condition monitoring and fault diagnosis systems are required for machinery. Rolling bearing faults are the most common faults in industrial equipment. Effectively finding and diagnosing rolling bearing faults can not only ensure its reliability, but also reduce maintenance costs. The general technology of...

AI Technical Summary

Problems solved by technology

Once the bearing is in a fault state, the entire mechanical equipment may fail at any time, causing major accidents and major economic losses

This classification method is not conducive to the early detection and elimination of faults

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