Novel topology convolutional neural network fault diagnosis for aircraft actuators and their sensors

Abstract

The variable working conditions and frequent turns make the aircraft actuator system prone to failure, seriously threatening flight safety. The identification of the airplane actuator system is critical for flight decisions and safety. Most fault diagnosis methods of actuators only focus on the actuators themselves, ignoring the disturbance caused by the fault of the actuator position sensor, which may easily lead to wrong decisions. In order to distinguish the actuator fault from its position sensor fault and identify the fault type accurately, an offline diagnosis method of convolutional neural network (CNN) with novel topology for processing time series is proposed. A new shift layer is added after the input layer, which avoids the loss of a large number of features due to the direct connection between the time series and the convolutional layer. A local topological network learning complex pattern with inception module is designed to improve the diagnostic accuracy in different working conditions. The wide residual structure is introduced to expand the convolutional channel, which allows the network features at the bottom level to propagate directly to the top level to prevent network degradation. Simulation results show that this method can accurately diagnose the actuator fault and its position sensor, with an average accuracy of 96.8%. Compared with the current mainstream data-driven methods, the precision and recall are increased by 6.3% and 6.7% respectively.

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

“…In recent years, with the mature development and application of deep learning methods such as Long short-term memory (LSTM) neural network technology [ 19 ] and convolution neural network (CNN) technology [ 20 ], some researchers have conducted valuable research in the field of aircraft failure rate prediction because of its advantages in data feature extraction. However, the deep learning model has theoretical limitations, resulting in many deficiencies in practical applications, such as large training samples, time-consuming, complex structure, difficult to determine its structural parameters, and premature convergence.…”

“…Regression analysis [1], time series [2,3], mathematical statistics [4], Weibull distribution statistics [5], Bayesian [6] Grey model GM (1, 1) [7][8][9], Verhulst [10] Machine learning model Artificial neural network (ANN) [11], BP neural network [12][13][14], generalized regression neural network (GRNN) [15], support vector machine (SVM) [16], least squares support vector machine (LS-SVM) [17], random forest [18] Deep learning model Long short-term memory (LSTM) [19], convolutional neural network (CNN) [20] Combined model…”

“…. , X (1) (20) 􏼈 􏼉, and generate X (1) by one-time progressive subtraction (1-iAGO). Conduct modeling according to the steps described in section 3.2.2, and obtain the corresponding prediction model data a � 0.06, B � 0.01 by using the grey system modeling software (v7.0) of China Southern Airlines.…”

Data‐driven sensor fault detection and isolation of nonlinear systems: Deep neural‐network Koopman operator

“…Besides, they need different types of faulty data to obtain classifiers for achieving fault isolation as in refs. [17,[27][28][29][30][31][32]. On the other hand, the methods which do not require faulty data are only concerned with the detection, like in refs.…”

Real-Time Diagnosis and Fault-Tolerant Control of a Sensor Single Fault Based on a Data-Driven Feedforward-Feedback Control System

“…It is worth noting that the neural network fault diagnosis method is based on the comparison between the predicted value and the actual value to achieve the fault diagnosis. The predicted value requires a large number of sample signals to train the algorithm, which is very difficult in engineering practice [15].…”