Prognostics for an actuator based on an ensemble of support vector regression and particle filter

The accurate prognostics for actuator malfunctions is a challenging task. Developing reliable prognostics methods is vital for providing reasonable preventive maintenance. Particle filter has been proved to be a prevailing approach to cope with actuator prognostics problems. However, the measurement function in the particle filter algorithm cannot be obtained in the prediction process. To this end, this article presents a hybrid framework combining support vector regression and particle filter. To accomplish the accurate prognostics for actuator fault of civil aircraft and provide the reliable “measurements” for the subsequent particle filter algorithm, the traditional support vector regression algorithm needs to be improved, and the error confidence level is imported to evaluate the usability of the support vector regression prediction output quantitatively. In addition, an improved particle filter based on Kendall correlation coefficient is put forward to address the problem of particles’ degeneracy. The experimental results are presented, demonstrating that the support vector regression–particle filter hybrid framework has satisfactory performance with better prognostics accuracy and higher fault resolution than traditional approaches