Abstract
The faults of the landing gear retraction/extension(R/E) system can result in the deterioration of an aircraft’s maneuvering conditions; how to identify the faults of the landing gear R/E system has become a key issue for ensuring aircraft take-off and landing safety. In this paper, we aim to solve this problem by proposing the 1-D dilated convolutional neural network (1-DDCNN). Aiming at developing the limited feature information extraction and inaccurate diagnosis of the traditional 1-DCNN with a single feature, the 1-DDCNN selects multiple feature parameters to realize feature integration. The performance of the 1-DDCNN in feature extraction is explored. Importantly, using padding dilated convolution to multiply the receptive field of the convolution kernel, the 1-DDCNN can completely retain the feature information in the original signal. Experimental results demonstrated that the proposed method has high accuracy and robustness, which provides a novel idea for feature extraction and fault diagnosis of the landing gear R/E system.
Highlights
The landing gear R/E system is the significant subsystem for aircrafts, after long-term running under complex and variable conditions, with heavy loads and strong impact, the key parts in the landing gear R/E system will inevitably generate multifarious faults, which may affect take-off, landing, and flight safety.Firstly, Hinton proposed a deep learning method in 2006, which set off a new wave of research on artificial intelligence and its applications [1]
In order to solve the above problems, this paper proposes a fault diagnosis method for the aircraft landing gear R/E system based on a 1-D dilated convolutional neural network
According to Equation (7), once the expansion factor is determined, the parameter that has a decisive influence on the receptive field size is the convolution kernel size
Summary
The landing gear R/E system is the significant subsystem for aircrafts, after long-term running under complex and variable conditions, with heavy loads and strong impact, the key parts in the landing gear R/E system will inevitably generate multifarious faults, which may affect take-off, landing, and flight safety. Hinton proposed a deep learning method in 2006, which set off a new wave of research on artificial intelligence and its applications [1]. Deep learning models have shown significant success in image processing, speech recognition, target detection, information retrieval, natural language processing, and so on [2]. As an important network structure, CNNs are widely applied in computer vision and natural language processing [3]. Machine learning methods have made great progress in the field of fault diagnosis. Gligorijevic et al proposed a method for rolling bearing fault diagnosis.
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