Convenient gearbox fault diagnosis under random variable speeds: A motor current nonlinear harmonic approach

Abstract

Motor current signature analysis (MCSA) techniques are gradually utilized for gearbox fault detection, given the convenience of current clamp installation and the clarity of signals. However, applying the MCSA to gearbox fault diagnosis presents substantial challenges. Specifically, fault features can be overwhelmed by both the fundamental frequency of the current and complex sidebands, and are susceptible to variations under variable speed conditions. To address these issues, an analytical model of current signals for localized gear faults is established, and a time–frequency analysis method is proposed for diagnosing gear faults. First, considering both amplitude modulation and frequency modulation effects, an analytical model of d-axis current signals under fault is established, which helps mitigate the influence of current fundamental frequency and complex sidebands. Second, a gear fault detection method called Iterative Vold-Kalman Filter is proposed, which combines the surrogate test with the Vold-Kalman Filter to solve the problem of fault representation under variable speed conditions. Finally, the proposed method is verified by simulated and experimental data from gearbox fault cases, and compared with classical algorithms to highlight the superiority of the proposed method. Overall, the proposed method enables quick and accurate detection of gear faults under variable speed conditions. © 2024 Elsevier Ltd