Fault Detection of Gearbox from Inverter Signals Using Advanced Signal Processing Techniques

The gear faults are time-localized transient events so time-frequency analysis techniques (such as the Short-Time Fourier Transform, Wavelet Transform, motor current signature analysis) are widely used to deal with non-stationary and nonlinear signals. Newly developed signal processing techniques (such as empirical mode decomposition and Teager Kaiser Energy Operator) enabled the recognition of the vibration modes that coexist in the system, and to have a better understanding of the nature of the fault information contained in the vibration signal. However these methods require a lot of computational power so this paper presents a novel approach of gearbox fault detection using the inverter signals to monitor the load, rather than the motor current. The proposed technique could be used for continuous monitoring as well as on-line damage detection systems for gearbox maintenance.