A looseness identification approach for rotating machinery based on post-processing of ensemble empirical mode decomposition and autoregressive modeling

Abstract

The purpose of this research is to investigate the feasibility of utilizing the post-processing of Ensemble Empirical Mode Decomposition (EEMD) and Autoregressive (AR) modeling to identify the looseness faults at different mechanical components of rotating machinery. The post-processing of EEMD is employed to decompose the complicated vibration signals of rotating machinery into a finite number of Intrinsic Mode Functions (IMFs) which represent the mono-oscillated components of different frequency bands. The AR modeling process reveals the fault features of the information-contained IMF components. The characteristic vectors derived from the AR modeling process can be utilized for identifying the locations of looseness faults in the operating machinery. A rotor-bearing test rig is performed to illustrate the looseness faults at different mechanical components. The analysis results show that the proposed approach is capable of identifying the locations of looseness faults at the rotating machinery.