Transient feature identification from internal encoder signal for fault detection of planetary gearboxes under variable speed conditions

Abstract Due to the harsh circumstances, planetary gearboxes as the important transmission component of mechanical equipment inevitably incur unexpected failures. Due to the merits of encoder signal, the research of encoder signal for fault detection have recently received much attention. However, the research on how to apply encoder signal to the fault detection of planetary gearbox under variation speed conditions is still not sufficient. Therefore, this paper proposes a three-stage variable speed encoder signal analysis approach for extracting fault-related transient features in original encoder signal and detecting potential fault of planetary gearboxes. In the proposed method, we first employ difference method to original encoder signal to convert it into more meaningful instantaneous angular speed (IAS) in time domain. Then, self-resampling technique is introduced to transform IAS in time domain into the new one in angular domain. At last, low-pass filter and sparsity based algorithm (LpfSpaA) is established for separating fault-induced IAS fluctuation from noisy IAS in angular domain. In the proposed LpfSpaA that is the core of the paper, a unique convex optimization problem is constructed and an iterative algorithm is derived to solve it. Meanwhile, to obtain the perfect performance of proposed LpfSpaA, an adaptive parameter determination scheme is also analyzed. The efficacy of proposed method in feature extraction and fault detection is assessed using synthetic and actual signals.