Study on two-step mechanical fault diagnosis integrating symbolisation method based on the division of probability density space and support vector machine.

In the traditional mechanical fault diagnosis, the number of normal samples and various fault samples is the same, which shields the adverse effects of sample imbalance on the fault diagnosis results. However, in actual industrial applications, the difference between the number of normal samples and the number of various fault samples is very different, so the problem of sample imbalance is unavoidable.The imbalance of the samples will lead to the tilt of the classification hyperplane of the support vector machine. There are similar problems in other classification methods, resulting in the generalisation ability of the training model is not strong.This paper proposes a two-step mechanical fault diagnosis integrating symbolisation method based on the division of probability density space and support vector machine. First, the normal samples are stripped out by the symbolisation method based on the division of probability density space; then, the support vector machine is applied to classify the fault samples. This method is applied for bearing fault diagnosis. The diagnostic accuracy increases by 3.34% compared to the single-step diagnosis with support vector machine. [ABSTRACT FROM AUTHOR]