Sinusoidal FM patterns of fault-related vibration signals for planetary gearbox fault detection under non-stationary conditions

Abstract Vibration signals related to planetary gearbox faults under non-stationary conditions will be equipped with sinusoidal modulation laws in their amplitude modulation (AM) and frequency modulation (FM) modes. Extracting fault features implied in the AM and FM modes is the key issue to detect inner gear faults occurring in planetary gearboxes. However, there exist few signal analysis approaches that are able to track the fast oscillating nature implied in the fault-induced sinusoidal FM patterns. To address such a challenging issue, this paper conducts a detailed study on sinusoidal FM patterns of the fault-related vibration signal model and proposes a novel method to extract them successfully. To be specific, the proposed method firstly separates the AM and FM modes via Hilbert transform to avoid their mutual interference. The intrinsic modulation features implied in the AM and FM modes can then be separately extracted by a developed signal decomposition method named iterative-joint chirp mode decomposition (I-JCMD) with a composite mode optimization scheme. Compared with the existing fault detection techniques, the proposed method is not only able to avoid the complicated sideband analysis but also identify the fast and time-varying oscillating nature of the FM mode with a high accuracy. The performance of our method is finally verified by three simulated cases and four groups of real vibration signals related to four types of sun gear faults occurring in a planetary gearbox test-rig, which also reveals a potential scaling relation between load strength and the oscillating coefficient of the FM mode.