An approach to fault diagnosis for gearbox based on reconstructed energy and support vector machine

Normally sensors can only be mounted on the outer shell of gearbox, which induce more difficulties to diagnose gearbox such as serious noise contamination, signal coupling and transmission path effect. Taking into account the unique structural characteristics of gearbox, this paper presents a novel method of using reconstructed energy and Support Vector Machine (SVM) to diagnose various failure or fault modes of gears, shafts and bearings. First, FFT is performed to get the frequency domain information of raw vibration signals. Then, a series of reconstruction filters are designed to remove unwanted information and enhance signal components of interest, which correspond to specific fault information of various elements. Finally, SVM is utilized to classify different faults such as bent shaft, broken gear and defect bearing. The proposed approach has proved to be effective in solving gearbox faults classification of the 2009 PHM Conference Data Analysis Competition.