Early Fault Warning and Identification in Condition Monitoring of Bearing via Wavelet Packet Decomposition Coupled With Graph

It has been well known that the fault of rolling element bearings (REBs) is one of the biggest causes for machine breakdown; thus, the early detection and type identification of a fault during REBs successive operations is necessary to help users take predictive maintenance action and/or schedule in order to avoid major machine failures. Wavelet packet decomposition (WPD) is a widely used technique to analyze vibration signal for health monitoring of REBs, but the resulting wavelet packet coefficients (WPCs) are still in high dimensionality, making them impossible for the direct use in practical usage. Keeping along the research line of high-level analysis for WPD enhancement, this article presents a new dynamic modeling approach, called graph-modeled wavelet packet coefficients (GMWPCs), that integrates WPD and graph theory in order to extract the correlation information between WPCs. By means of an adaptive input weight fusion, the GMWPCs can automatically confirm the weight of the frequency sub-band where the fault-induced information is more evident. By virtue of GMWPCs, a new two-phase framework for early warning detection and fault identification is proposed finally, which is able to not only detect the time location of the fault in its very early stage but also identify the fault type. We conducted different experiments to validate the early warning detection and fault identification separately. Experimental results, outperforming the state of the arts, verify the adequacy and effectiveness of the proposed framework and meanwhile reveal its appropriate use for real engineering applications.