LAUSR

Abstract In modern rotating machinery, rotary encoders have been widely used for the purpose of positioning and dynamic control. The study in this paper indicates that, the encoder signal, after proper processing, can be also effectively used for the health monitoring of rotating machines. In this work, a Kurtosis-guided local polynomial differentiator (KLPD) is proposed to estimate the instantaneous angular speed (IAS) of rotating machines based on the encoder signal. Compared with the central difference method, the KLPD is more robust to noise and it is able to precisely capture the weak speed jitters introduced by mechanical defects. The fault diagnosis of planetary gearbox has proven to be a challenging issue in both industry and academia. Based on the proposed KLPD, a systematic method for the fault diagnosis of planetary gearbox is proposed. In this method, residual time synchronous time averaging (RTSA) is first employed to remove the operation-related IAS components that come from normal gear meshing and non-stationary load variations, KLPD is then utilized to detect and enhance the speed jitter from the IAS residual in a data-driven manner. The effectiveness of proposed method has been validated by both simulated data and experimental data. The results demonstrate that the proposed KLPD-RTSA could not only detect fault signatures but also identify defective components, thus providing a promising tool for the health monitoring of planetary gearbox.