A bearing fault and severity diagnostic technique using adaptive deep belief networks and Dempster–Shafer theory

Abstract

An artificial intelligent bearing fault and hierarchical severity diagnosis framework is proposed in this study. The framework utilizes a combined deep belief networks (DBNs) and Dempster–Shafer (D-S) theory fault diagnosis scheme and adopts a two-stage approach in classifying (1) bearing fault conditions and (2) fault severities. The combined fault diagnostic scheme first employs two parameter-optimized DBNs to process the horizontal and vertical vibration data acquired from the bearing house of a test rig, where the parameters of the DBNs are optimized using a hybrid genetic algorithm and particle swarm optimization algorithm proposed in this study. The classification results from the two DBNs are fused further using the D-S theory to improve the diagnostic accuracy. The fault diagnosis scheme is used first to classify the bearing fault conditions in Stage 1 from a bulk dataset containing all bearing operation conditions under study. The same diagnosis scheme is applied once more to classify the hierarchical fault severities for each fault condition in Stage 2 using the pre-classified data from Stage 1. The effectiveness of the framework is then evaluated on a set of bearing condition monitoring data. A comparison study between the results obtained using the current method and those from existing published work is also presented in the article. It is shown that the accuracy for bearing fault and severity diagnosis can be substantially improved by using the current framework.