Yimin Zhan

TL;DR: In this article, the authors proposed a novel technique for state detection of gearbox, which fits a time-varying autoregressive model to the gear motion residual signals applying a noise-adaptive Kalman filter, in the healthy state of the target gear.

TL;DR: In this article, a robust model-based technique for the detection and diagnosis of gear faults under varying load conditions using the gear motion residual signal was presented, where a noise-adaptive Kalman filter-based auto-regressive (AR) model was fitted to the residual signals in the healthy state of the gear of interest.

TL;DR: In this paper, the authors proposed three adaptive parametric models transformed from time-varying vector-autoregressive model with their parameters estimated by means of noise-adaptive Kalman filter, extended KF and modified KF, respectively on the basis of different assumptions.

TL;DR: In this article, a state-space model of non-stationary multivariate vibration signals for the online estimation of the state of rotating machinery using a modified extended Kalman filtering algorithm and spectral analysis in the time-frequency domain is proposed.

TL;DR: In this article, three adaptive parametric models based on three advanced adaptive filtering algorithms were investigated for on-line condition monitoring of rotating machinery, and the test results demonstrate that the optimum filter behavior can readily be attained and the white Gaussian assumption of innovations can relatively be easily guaranteed for the NAKF-based model under distinct gear states and a wide variety of model initializations.