Sensing Data-Based Degradation Estimation of Electromechanical Actuator Under Dynamic Operating Conditions

In flight control actuation systems, electromechanical actuators (EMAs) as energy-efficient components play critical roles in next-generation aircraft. Reliable degradation estimation models based on sensing data from sensors installed in EMA can help to improve its availability and safety, which need to be studied. However, the dynamic operating conditions, which will lead to the mismatch issues of the sensing data-based degradation estimation models, reduce the overall accuracy of the degradation estimation. To address this issue, an improved adaptive Wiener process model for EMA degradation estimation based on sensing data with dynamic operating conditions is proposed. First, a health indicator (HI) is extracted from sensing data through physical analysis or extracted using data processing techniques. Then, a novel model for EMA degradation estimation with the improved strong tracking filter and the Wiener process model (i.e., ISTF-WPM) is formulated based on the obtained HI. By introducing a saturation function to the improved strong tracking filter (STF), the performance of sensing data-based degradation estimation with dynamic operating conditions can be improved. The proposed model presents a novel routine for EMA degradation estimation based on sensing data with dynamic operating conditions. In the experiments of this study, the effectiveness of the ISTF-WPM is validated using simulation data as well as practical data collected by sensors of EMA. Experimental results indicate that EMA sensing data-based degradation estimation with the proposed model has a high overall accuracy.