Aircraft Actuator Fault Diagnosis Using Deep Learning Based Sparse Representation and TSM

This paper presents a novel data-based fault diagnosis approach of aircraft actuators by using deep learning methods. Electro-mechanical actuator (EMA), which we study on, widely used in a new generation of aircraft serves as the research object. The basic fault diagnosis framework of this work is based on Time Series Modeling (TSM), which builds a model for each sensor data, then faults can be detected and isolated by differences between model prediction result and measured result. In this structure, the quantity of sensors is directly related to the fault diagnosis effect, that is, the fault detection capability increases when the number of sensors increases. Simultaneously, the workload of the model training increases without expectation. Therefore, the reduction of sensors is of great significance. The traditional method is to use the manual experience to screen, but this method has high requirements on personal ability and performance is not easy to guarantee. This paper adopts the Sparse Auto-Encoder (SAE) algorithm, which avoids the simple direct screening process and achieves the purpose of reducing the number of sensors by adaptively extracting features. Important features of sensor data, preserved by loss compression using SAE, can be used to build time series models. Relationship of time series adjacent data is adopted to build mathematical models. Compared with traditional machine learning algorithms, recurrent neural network (RNN) algorithm can make good use of the relationship between time series, which has been widely used in speech recognition, text recognition, and other fields. However, standard RNN algorithm tends to ignore future information. In this paper, the Bidirectional Long Short-term Memory RNN algorithm (BiLSTM-RNN), considering both past data and future data, is applied to TSM. Through the Comparison of the standard RNN and long short-term memory RNN (LSTM-RNN) algorithms, the biLSTM-RNN algorithm shows better modeling and fault diagnosis performance.