Fault diagnosis for rolling bearings based on generalised dispersive mode decomposition and accugram

Abstract

Bearing fault diagnosis helps to ensure the safe operation of electromechanical equipment and reduce unnecessary losses due to downtime. The interference of noise in the signal poses a challenge in the effective identification of rolling bearing faults. To address the above problems, this paper proposes a rolling bearing fault diagnosis (RBFD) method based on generalised dispersive mode decomposition (GDMD) and an accugram. Firstly, the bearing signal is decomposed using GDMD and the optimal number of decomposition modes is chosen using a new index based on the correlation coefficient and accuracy. According to the number of determined decomposition modes, the fault signal is reconstructed. Then, the centre frequency and bandwidth of the resonant frequency are determined using an accugram. Finally, the fault signal is filtered and analysed using a square envelope spectrum to achieve rolling bearing fault diagnosis. Experimental signal analysis verifies the effectiveness and feasibility of the method. The method is applied to the early fault diagnosis of rolling bearings and compared with kurtogram and accugram results. The results show that the approach can not only effectively avoid the interference of external impacts but it can also correctly recognise the fault characteristic frequency band.