Incipient fault intelligent monitoring and diagnosis based on fuzzy support vector data description

In order to solve the problems of correctly identifying incipient fault and accurately monitoring fault development for electromechanical equipment, a new method of incipient fault intelligent monitoring and diagnosis based on fuzzy support vector data description (FSVDD) is proposed. With this method, one-class classifier can be built when only the information of the target class is available, and the outlier objects can be hierarchically distinguished from target objects when these membership degrees of outlier objects are appended to the kernel function. The proposed method is applied to the condition monitoring and fault diagnosis of electromechanical equipment, which can detect incipient fault only using normal condition signals and identify the fault severity. The experimental result shows that this method not only fast detects the bearing incipient fault, but accurately identifies the fault severity.