Equipment health state evaluation and recession prediction method based on multi-channel sensing signals

Abstract

The invention relates to an equipment health state evaluation and recession prediction method based on multi-channel sensing signals. The operation steps of the method are as follows: the first step: joint noise filtration of the multiple sensing signals: adopting a multi-scale wavelet filtering algorithm to perform the joint noise filtration on the multi-channel sensing signal collected from equipment so as to improve the quality of the multiple sensing signals; the second step: generation of an original characteristic set; the third step: extraction of local characteristics of the signals; the fourth step: modeling of a reference self-organizing mapping model; and the fifth step: health quantitative evaluation and residual life prediction. By adopting the method, the quantitative evaluation and the life prediction of performance recession of the equipment can be realized, the operation reliability of the equipment can be further improved and the maintenance cost can be reduced.

Description

technical field [0001] The invention is a method for evaluating and predicting the health state of equipment based on multiple sensing signals, involving joint noise filtering of multiple sensing signals, generation of original feature sets, local feature extraction, benchmark self-organizing mapping model modeling, health quantitative assessment and Remaining life prediction, realizing real-time quantitative evaluation of equipment health status and prediction of decline trend. The invention belongs to the technical field of modern equipment state monitoring and health prediagnosis and maintenance. Background technique [0002] At present, key equipment presents a development trend of high automation, high precision, high reliability and high intelligence, emphasizing the controllability, reliability and maintainability of equipment. The sudden occurrence of equipment failure will not only greatly increase the maintenance cost of the enterprise, but also seriously affect t...

AI Technical Summary

Problems solved by technology

The system has the following deficiencies: [1] It is useless to perform multi-sensing signal joint noise filtering processing on the collected multi-sensing signal, which may cause the signal to contain more noise; [2] The principal component analysis method is only used in the feature extraction stage. The feature of the maximum global variance information in the data set can be extracted, but the information with the largest local variance cannot be effectively extracted, and important information may be lost; [3] This method only evaluates the degradation of equipment performance, but does not predict the future health of the equipment. Trends and Implementation of Effective Remaining Life Prediction

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