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Predicting the degradation process of bearings before they reach the failure threshold is extremely important in industry. This paper proposed a novel method based on the support vector machine (SVM) and the Markov model to achieve this goal. Firstly, the features are extracted by time and time-frequency domain methods. However, the extracted original features are still with high dimensional and include superfluous information, and the nonlinear multifeatures fusion technique LTSA is used to merge the features and reduces the dimension. Then, based on the extracted features, the SVM model is used to predict the bearings degradation process, and the CAO method is used to determine the embedding dimension of the SVM model. After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy. The proposed method was validated by two bearing run-to-failure experiments, and the results proved the effectiveness of the methodology.

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In this paper, the theory of how to use an improved genetic algorithm to optimize the stimulus parameters of frequency testing in analog diagnose is studied. We code the parameters of a stimulus into a chromosome, and we use the genetic operators such as reproduction, crossover and mutation to create new stimulus. To improve the computation efficiency and reduce the possibility of trapping into the local optimums, the probability crossover and probability mutation is adaptive-self to the search process.

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