Multi-fault diagnosis of ball bearing based on features extracted from time-domain and multi-class support vector machine (MSVM)

Due to the importance of rolling bearings as one of the most populous used industrial machinery elements, development of proper monitoring and fault diagnosis procedure to suppression malfunctioning and failure of these elements during operation is necessary. For rolling bearing fault detection, it is expected that a desired time domain analysis method has good computational efficiency. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with extracting features in time-domain from the vibration signals and multi-class support vector machine (MSVM) that used to the detection and classification of rolling-element bearing faults. The roller bearings nature of vibration reveals its condition and the features that show the nature are to be extracted through some indirect means. The method consists of two stages. Firstly, the features in time-domain from the vibration signals, which are widely used in fault diagnostics, are extracted. Finally, the features that extracted are classified successfully using MSVM classifier and the work condition and fault patterns of the roller bearings and then faults are diagnosis real tine based on Voting.