Degradation Estimation of Electro-Mechanical Actuator With Multiple Failure Modes Using Integrated Health Indicators

• Published in: IEEE sensors journal Vol. 20; no. 13; pp. 7216 - 7225
• Main Authors: Zhang, Yujie, Peng, Yu, Liu, Liansheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuation; Actuators; Aging aircraft; Computer simulation; Degradation; degradation estimation; Diagnostic systems; Electro-mechanical actuator; Estimation; Failure modes; Flight control; Fly by wire control; Generalized linear models; health indicator; Hidden Markov models; Markov chains; Markov processes; Monitoring; multiple failure modes; Performance degradation; Performance evaluation; Prognostics and health management; Queuing theory; Sensors; Statistical models
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.2978140

Electro-Mechanical Actuator (EMA) is a key component in the flight control actuation of the more electric aircraft. The degradation of EMA has a significant influence on its performance. Hence, the degradation estimation of EMA needs to be studied, which is an important part of the health monitoring of EMA. However, the multiple failure modes of EMA bring much difficulty for implementing degradation estimation. To solve this problem, a fusion degradation estimation method based on Continuous Hidden Markov Model (CHMM) and Generalized Linear Model (GLM) (i.e., Multiple CHMM-GLM) is proposed for EMA with multiple failure modes. Firstly, Health Indicator (HI) extraction models are formulated based on CHMM for multiple failure modes to extract HIs, which can be used to indicate the degradation degree of EMA. Secondly, the fusion degradation model for EMA with multiple failure modes is formulated based on the extracted HIs and GLM, in which the nonlinearity of a single HI and the coupling among multiple HIs are both considered. Finally, the output of the fusion degradation model is regarded as a HI to indicate the degradation degree of EMA. This study provides a new way for the degradation estimation of EMA with multiple failure modes, which can contribute to the Prognostics and Health Management (PHM) development of EMA. To evaluate the effectiveness of the proposed method, experiments are conducted utilizing EMA simulation and practical data. Experimental results show that the proposed method has a good performance in the degradation estimation for EMA with multiple failure modes.