Improved wavelet threshold denoising-based fault diagnosis method and improved wavelet threshold denoising-based fault diagnosis system

Abstract

The invention discloses an improved wavelet threshold denoising-based fault diagnosis method and an improved wavelet threshold denoising-based fault diagnosis system. The method comprises the steps ofobtaining to-be-diagnosed TE process data; standardizing the acquired data; carrying out wavelet transform decomposition on the standardized data, decomposing the data into a plurality of layers, andobtaining a wavelet coefficient of each layer; for the wavelet coefficient of each layer, calculating a skewness coefficient and a kurtosis coefficient; according to the skewness coefficient and thekurtosis coefficient of each layer, judging whether the wavelet coefficient of the current layer conforms to normal distribution or not is judged, and if yes, a hard threshold method is adopted for denoising; otherwise, denoising by adopting a soft threshold method; after denoising, obtaining a processed high-frequency coefficient and a processed low-frequency coefficient of each layer of waveletcoefficient, and reconstructing a data signal by using the processed high-frequency coefficient and the processed low-frequency coefficient to obtain denoised data; and analyzing the denoised data byutilizing a PCA model, and judging whether the TE process data to be diagnosed has fault data or not.

Description

technical field [0001] The present disclosure relates to the technical field of TE process fault diagnosis, in particular to a fault diagnosis method and system based on improved wavelet threshold denoising. Background technique [0002] The statements in this section merely mention background art related to the present disclosure and do not necessarily constitute prior art. [0003] Since the 21st century, with the rapid development of science and technology, modern industrial systems have become larger and more complex, and there is an urgent need to improve the safety and stability of the production process, so it is necessary to detect faults accurately and timely. The fault diagnosis method of multivariate statistics is generally adopted because it does not need to understand professional process knowledge and complex mathematical models, and only relies on collected industrial production process data. The principal component analysis method is a powerful technique for...

AI Technical Summary

Problems solved by technology

But there are also some disadvantages in these methods: for example, the soft threshold method has a constant deviation, which will cause signal distortion; the signal after the hard threshold method is not smooth after noise reduction, and will generate additional oscillations

[0017] (1) The traditional threshold method fixedly adopts the soft threshold method or hard threshold method on each scale, and does not consider the selection based on the characteristics of the wavelet coefficients of each layer Appropriate threshold function, lack of flexibility, can not make full use of the advantages of soft threshold method and hard threshold method

[0018](2) The real data obtained in the actual industrial production process is essentially multi-scale, so the PCA fault diagnosis method cannot be applied to the fault diagnosis research of multi-scale data

The multi-directional principal component analysis method overcomes the defect that the traditional PCA method can only diagnose sample data on a single scale

The traditional multi-scale principal component analysis method has the defects of weak ability to analyze and detect process changes and is easily disturbed by noise data and random errors.

Images