Research And Application Of Equipment Fault Diagnosis Technology Based On LR And GMM

In recent years, as modern manufacturing industry develops, various kinds of mechanical equipments have been widely utilized in petro-chemical, electricity industries and so on, while the equipments concerned have the trend towards large scale, intelligentization, high speed and integration, which greatly enhances the progress of the social productivity. However, if faults occur in these equipments, it will not only cause economic losses to the enterprises, but also will result in severe human casualties. As a result, to research equipment fault diagnosis technology and guarantee the safe and stable operation of the equipments has been of extreme significance to modern enterprises.This paper is supported by National Key Technologies R& D Program of China:(2006BAK02B02), and the main jobs of this study are as follows:(1) Introduced the background, goal and significance of this research, illustrated the current developing status of performance degradation assessment and fault pattern recognition method for equipments, and ascertained the problem to be solved.(2) Researched signal processing, feature selection and feature extraction technology. Signal processing methods including FFT, Wavelet Transform and Wavelet Packet Decomposition are paid special attention to. Meanwhile, feature selection method based on fault characteristic frequency and feature extraction method based on principal component analysis are both thoroughly investigated.(3) Investigated logistic regression and Gaussian mixture model that are theoretically essential in this paper. As for LR, its theoretical illustration and parameter estimation based on MLE (maximum likelihood estimation) are introduced; as for GMM, its theory and parameter estimation based on EM algorithm are introduced. In addition, this paper explored the problem of parameter initialization and optimal mixture number for GMM.(4) Introduced LR and GMM into the performance degradation assessment research of rotating machinery. LR is used to assess the performance of bearing, and the method is validated by both experimental data and CWRU data; GMM is utilized to assess the performance of centrifugal compressor, which is validated by data acquired in the field of a chemical refinery enterprise of Petro China. Both the two methods have gained favorable effect.(5) GMM is also introduced in the fault pattern recognition for rotating machinery. Firstly, traditional Bayesian classifier-based method is adopted, and then an improved method based on the overlap between GMMs is proposed, which have been validated by CWRU bearing data.