Aircraft is becoming more precise, integrated, high-speed, informationalized and intelligent. The key mechanical parts of aircraft will inevitably generate multifarious faults due to the severe working conditions with high temperature, fast speed, heavy load, large disturbance and strong impact. The faults of aircraft key parts often show some characteristics such as weakness, randomness, coupling, diversity, uncertainty and so on. On the other hand, considering that the amount of the key parts is great and the number of the monitoring sensors is large, massive data will be acquired through high sampling frequency after long term service, making aircraft fault diagnosis enter the era of big data. Therefore, using the traditional methods based on advanced signal processing techniques, feature extraction and feature selection, it is a great challenge to diagnose the various faults of aircraft key parts. As a very promising tool in the field of intelligent fault diagnosis, deep learning can largely get rid of the dependence on manual feature design and engineering diagnosis experience. Four kinds of popular deep learning models, including deep belief network, convolutional neural network, deep auto-encoder and recurrent neural network, are respectively introduced and used for intelligent fault diagnosis and prognosis of mechanical parts. The results confirm that deep learning models are able to automatically capture the representative information from the massive measured data through multiple feature transformations, and directly establish the accurate mapping relationships between the raw data and various operation conditions.
School of Aeronautics, Northwestern Polytechnical University, China
Hongkai Jiang received the Ph.D. degree in instrument science and technology from the Mechanical Engineering School, Xi’an Jiao Tong University, Xi’an, China, in 2006. He is currently a Full Professor in the School of Aeronautics, Northwestern Polytechnical University, Xi’an, China. From January 2011 to January 2012, he was a Visiting Scholar with the University of British Columbia, Vancouver, Canada. His current research interests include intelligent maintenance and health management, deep learning and big data analysis, and fault diagnosis and dynamic signal processing.