Enhanced order spectrum analysis based on iterative adaptive crucial mode decomposition for planetary gearbox fault diagnosis under large speed variations

Abstract

Variable speed conditions are omnipresent in the operation of rotating machinery. Tacholess order tracking (TOT) technique based on time-frequency representation (TFR) has been attracting much attention in recent years, but the challenges of low time-frequency resolution, parameter selection and computational consumption limit its practical applications. To this end, this paper presents a novel time-frequency analysis (TFA) method named iterative adaptive crucial mode decomposition (IACMD), which accurately extracts the time-varying component in the signal through the instantaneous frequency refinement strategy and the adaptive bandwidth parameter update rule. Then, an enhanced order spectrum analysis scheme based on IACMD is further proposed, which highlights the fault characteristic order by eliminating the amplitude modulation effect and is completely independent of the tachometers. Both simulation analysis and test-rig experiments have demonstrated the effectiveness of the proposed strategy in fault diagnosis of planetary gearboxes under various large speed variations. Compared with traditional TFA methods, the results show that the IACMD provides higher accuracy and efficiency even in the case of large time-varying and strong noise.