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.

Aiming at the difficulty of mining the prediction starting point and constructing the prediction model for the Remaining Useful Life (RUL) of rolling bearings, a RUL prediction method is proposed based on Health Indicator (HI) and Trajectory Enhanced Particle Filter (TE-PF). By extracting the HI that can accurately track the trend of bearing degradation and combining it with the early fault enhancement technology, the early abnormal sample nodes can be mined to provide more samples with fault information for the construction and training of subsequent prediction models. The prediction of RUL with probability and statistics can be realized based on PF. However, traditional degradation rate models based on PF are vulnerable to HI mutations. Aiming at this problem, the TE-PF prediction method is proposed based on comprehensive utilization of historical degradation information to modify the prediction model parameters timely. The results of the rolling bearing test show that the prediction starting point can be mined accurately and a scientific prediction model can be constructed conveniently by the RUL prediction method based on HI and TE-PF. Furthermore, aiming at the RUL prediction problem under the condition of HI mutation, the RUL prediction with probability and statistics characteristics under the confidence interval can be obtained based on the method proposed.

Electromechanical actuators (EMAs), as the critical actuator system of next-generation aircraft, have attracted the attention of many institutions and enterprises around the world. However, due to harsh working conditions, their reliability cannot satisfy the requirements of widespread application in aircraft. Therefore, in order to conduct fault diagnosis on EMAs, in this paper, we establish a comprehensive dynamic model under numerous assumptions to study the fault characteristics that may occur in the displacement and acceleration responses of EMA systems. First, an eight-DOF dynamic model containing typical mechanical components of an EMA is established. Then, by obtaining the impact forces between balls and the spalling fault and the nonlinear relationship between the total elastic restoring forces and the change of ball deformation when the fault occurs, a faulty dynamic model is established. Comparison of the simulation results between the normal and faulty model reveals that the acceleration amplitude at the third harmonic of the ball passage frequency increases when fault occurs. Based on this phenomenon, a numerical calculation method of fault characteristics is proposed. Finally, the effectiveness of the established models and the identified phenomenon are verified by experiments conducted on an EMA test rig in a laboratory environment.

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