Optimal weight impulse extraction: New impulse extraction methodology for incipient gearbox condition monitoring

Abstract

Gear faults in a transmission system generally cause impulse components in vibration signals, which is a crucial symbol for gearbox fault diagnosis. However, their related signals are often interfered or even submerged by the noisy meshing components (NMC) of gearboxes in degradation, which introduces challenges for incipient fault detection and condition monitoring. Commonly employed deconvolution-based methods attempt to design a filter to extract impulse components. However, these methods fail to address the interference issue of the NMC on deconvolution process. To overcome this limitation, this paper proposes an optimal weight impulse extraction (OWIE) methodology to suppress the NMC and highlight impulse component in vibration signal. Different from deconvolution-based methods, the proposed method locates impulses adaptively driven by waveform itself. A non-impulse part is suppressed through point-to-point removal, while impulse components are highlighted by subtracting a weighted signal from a raw signal. An iterative procedure is utilized to solve the optimal weight sequence by maximizing the kurtosis of an impulse signal. The effectiveness of the proposed OWIE is validated through a simulation case study and a run-to-failure experiment of gearboxes. Results demonstrate that the OWIE is sensitive to incipient faults and is suitable for the health condition monitoring of gearboxes.