Vibration signals of complex rotating machinery are often cyclostationary, so in this paper one novel method is proposed to detect and predict early faults based on the linear (almost) periodically time-varying autoregressive (LPTV-AR) model. At first the algorithms of identifying model parameters and order are presented using the higher-order cyclic-cumulant, which can suppress additive stationary noises and improve the signal to noise ratio (SNR). Then numerical simulations are done and the results indicate that this model is more effective for cyclostationary signals than the classical AR model. In the end the proposed method is used for detecting incipient gear crack fault in a helicopter gearbox. The results demonstrate that the approach can be used to detect and predict early faults of complex rotating machinery by the kurtosis of the residual signal.
As More/All Electric Aircraft gradually become a research hotspot, electromechanical actuators (EMAs), which can directly convert electrical energy into mechanical energy, have gained more and more attention. However, since the reliability of EMA cannot meet the requirements of actual aircrafts, the practical application of EMA is severely limited. Therefore, fault diagnosis, prognosis and health management (DPHM), which can realise condition‐based maintenance (CBM), has become the key technology in the application of EMA. The aim is to summarise the research on EMA fault modes, fault diagnosis, prognosis and health management systematically and comprehensively. First, the basic structure and common fault modes of EMAs are introduced, and the failure mechanism of EMA is studied. Then, the algorithms of DPHM for EMAs are reviewed in detail. The perception strategies of data acquisition are analysed, and the EMA fault diagnosis methods, including model‐based and data‐driven methods, are reviewed. The research of remaining useful life (RUL) prediction and fault‐tolerant control are introduced. After that, some problems of the existing research on EMA DPHM and their potential solutions are put forward. Finally, several possible developing directions of research on EMA DPHM are predicted.
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