Multi-mode separation and nonlinear feature extraction of hybrid gear failures in coal cutters using adaptive nonstationary vibration analysis

Abstract

Reliable condition monitoring and fault diagnosis is an important issue for the normal operation of coal cutter gear systems. Intrinsic deterioration indicators are always hidden in the vibration response of the gearboxes, and it is often very difficult to correctly extract them due to nonlinear/chaotic nature of the vibration signal. Literature review suggests that hybrid gear faults diagnosis is a challenging task and how to extract quantitative indicators for hybrid faults detection is attracting considerable attentions. In order to address this issue, a new adaptive nonstationary vibration analysis method is proposed in this paper to extract useful quantitative indicators for hybrid gear faults decoupling detection. In this new technology, the center frequencies of the narrow bands of intrinsic modes contained in the vibration signal were adaptively estimated by the variational model decomposition (VMD) to determine the bandwidth of the modes. Hence, the hybrid gear faults were decoupled into single faults in the form of VMD modes. Then, the time and frequency features of each mode were calculated to obtain the feature space. Lastly, the feature space was projected into the reproducing kernel Hilbert space by the spectral regression-optimized kernel Fisher discrimination (SRKFD), where the instinct nonlinear structure in the original data can be identified and thus useful quantitative indicators can be extracted. Reliable hybrid faults decoupling detection was then achieved. Specially designed numerical simulations and experiments were conducted to evaluate the proposed VMD-SRKFD method on hybrid gear faults diagnosis of coal cutters. The performance was compared with existing techniques. The analysis results show high performance of the proposed method on quantitative hybrid faults detection in the coal cutter gear system.