Fault Diagnosis for Electro-Mechanical Actuators Based on STL-HSTA-GRU and SM

The electro-mechanical actuators (EMAs) play an important role in the new-generation aircraft, which makes the fault diagnosis of EMA become a hot topic in the industry. However, the EMA signals usually have nonlinear characteristics and seasonal tendency, which bring great challenge to the fault diagnosis. Furthermore, detecting faults in the early stage helps reduce the risk of serious damage to EMA, but most studies are focusing on the situation that the EMA faults are well-developed. To tackle the challenge, we present an innovative algorithm which combines a hybrid-spatial and temporal attention-based gated recurrent unit (HSTA-GRU) with Seasonal-Trend decomposition procedures based on Loess (STL) to predict multiple time-series data for more failure information. The STL extracts the seasonal factor for mitigating the influence of seasonal fluctuation, and the HSTA-GRU captures the spatio-temporal relationships among multivariate EMA sensors for a long-term prediction of multiple time-series data. Then, for the predicted time series, a similarity measure (SM) function based on dynamic time warping (DTW) is used to classify the fault types without training, so as to reduce the accumulated error and enhance the efficiency of classification. Ultimately, the analysis result on an experimental EMA fault dataset demonstrates that the proposed arithmetic can provide a superior performance not only in the time series prediction, but also for EMA fault diagnosis.

Automated summary

In this study, a novel algorithm combining HSTA-GRU and STL is proposed to predict multiple time-series data for enhanced fault diagnosis of EMAs. By extracting seasonal factors and capturing spatio-temporal relationships among multivariate EMA sensors, the algorithm improves time series prediction and fault classification efficiency.