Incipient fault feature extraction of main bearing by iterative squared envelope analysis

Extracting weak features of incipient bearing fault from the collected vibration of rotating system is the basis of the fault diagnostics of main bearing in the aero engine. To monitor the running condition of the main bearing, a novel weak feature extraction method for bearing fault, named as iterative squared envelope analysis (ISEA) is proposed by extracting the fault characteristic orders of bearings. Both simulations and experiments, involving the outer and inner race faults, are performed to validate the efficacy of ISEA. It is shown that the ISEA can efficiently eliminate the vibrations produced by rotor and extract the bearing fault feature. Compared with the result obtained by the cepstrum pre-whiten method, both amplitude and cyclic feature can be reserved closer to the true values than that obtained by the cepstrum pre-whiten (CPW) method. Therefore, the ISEA is more powerful in the weak feature extraction of bearings than the CPW method.