Hybrid data fusion approach for fault diagnosis of fixed-axis gearbox

Abstract

Intelligent fault diagnosis system based on condition monitoring is expected to assist in the prevention of machine failures and enhance the reliability with lower maintenance cost. Most machine breakdowns related to gears are a result of improper operating conditions and loading, hence leads to failure of the whole mechanism. With advancement in technology, various gear fault diagnosis techniques have been reported which primarily focus on vibration analysis with statistical measures. However, acoustic signals posses a huge potential to monitor the status of the machine but a few studies have been carried out till now. This article describes the implementation of Teager–Kaiser energy operator and empirical mode decomposition methods for fault diagnosis of the gears using acoustic and vibration signals by extracting statistical features. A cross-correlation-based fault index that assists the automatic selection of the sensitive Intrinsic Mode Function (IMF) containing fault information has also been described. The features extracted by all combinations of signal processing techniques are sorted by order of relevance using floating forward selection method. The effectiveness is demonstrated using the results obtained from the experiments. The fault diagnosis is performed with k-nearest neighbor classifier. The results show that the hybrid of empirical mode decomposition–Teager–Kaiser energy operator techniques employs the advantages traits of one or the other technique to generate overall improvement in diagnosing severity of local faults.