Fault diagnosis and prediction of complex system based on Hidden Markov model

To guarantee the performance and security of the complex system, in this paper, we focus on the problem of fault diagnosis and fault prediction method for the complex system. The proposed fault diagnosis and prediction system is made up of three parts: 1) Data preprocessing, 2) Degradation state detection, and 3) Fault diagnosis. Afterwards, we exploit the Wavelet transform correlation filter to extract features for complex system fault diagnosis and prediction. Particularly, the direct spatial correlations of wavelet transform contents are used to search the locations of edges. To promote the performance of Hidden Markov model, we propose a HMM-based semi-nonparametric method by the probabilistic transition frequency profile matrix and the average probabilistic emission matrix. Then, the training sequence which is the most similar to a particular sequence can be found by the modified HMM model. Finally, experimental results prove that the proposed algorithm can effectively enhance the accuracy of equipment fault diagnosis and equipment state recognition task.