EMA Health Indicator Extraction Based on Improved Multivariate State Estimation Technique With a Composite Operator

With the development of fly-by-wire controls, electro-mechanical actuators (EMAs) have become essential components for aircraft. To guarantee operational safety and reliability of EMA, prognostics, and health management (PHM) can be utilized to acquire reliable prediction information on potential failures before they occur. Furthermore, as a significant procedure of PHM, construction of health indicators (HIs) enables assessment of performance degradation. Due to the advantages of low-computational complexity and strong interpretability, the multivariate state estimation technique (MSET) has become one of the mainstream methods for HI extraction. However, given various operation conditions of EMA, it is difficult to select appropriate distance metric of MSET that accurately calculate health state of EMA, which will further lead to accuracy losses of HI. To solve above problem, an improved MSET with a composite operator (CO-MSET) for EMA HI extraction is proposed in this article. First, monitored parameters under different operation conditions of EMA are used to construct observation matrix and memory matrix. Second, a composite nonlinear operator with different optimization weights is introduced to calculate estimates. Finally, the output of extracted HI will be further obtained by calculating the residual vector. To validate the effectiveness of the proposed method, experiments are conducted on the dataset from NASA's flyable electro-mechanical actuator (FLEA). Experimental results illustrate that the proposed method has a better performance on HI extraction for EMA, which is suitable for EMA health state representation under various operation conditions.