Automotive Science and Engineering

Identifying fault categories, especially for compound faults, is a challenging task in mechanical fault diagnosis. For this task, this paper proposes a novel intelligent method based on wavelet packet transform (WPT) and multiple classifier fusion. An unexpected damage on the gearbox may break the whole transmission line down. It is therefore crucial for engineers and researchers to monitor the health condition of the gearbox in a timely manner to eliminate the impending faults. However, useful fault detection information is often submerged in heavy background noise. The non-stationary vibration signals were analyzed to reveal the operation state of the gearbox. The proposed method is applied to the fault diagnosis of gears and bearings in the gearbox. The diagnosis results show that the proposed method is able to reliably identify the different fault categories which include both single fault and compound faults, which has a better classification performance compared to any one of the individual classifiers. The vibration dataset is used from a test rig in Shahrekord University and a gearbox from Sepahan Cement. Eventually, the gearbox faults are classified using these statistical features as input to WSVM.