Applications of deep learning in big data analytics for aircraft complex system anomaly detection

Shungang Ning, Jianzhong Sun, Cui Liu and Yang Yi

Journal of Risk and Reliability, 2021, vol. 235, issue 5, 923-940

Abstract: Big data analytics with deep learning approach have attracted increasing attention in transportation engineering, involving operations, maintenance, and safety. In commercial aviation sectors, operational, and maintenance data produced on modern aircraft is increasing exponentially, and predictive analysis of these data is an exciting and promising field in aviation maintenance, which has a potential to revolutionize aerospace maintenance industry. This study illustrates the state-of-the-art applications of deep learning in big data analytics for predictive maintenance and a real-world case study for commercial aircraft. A Long Short-Term Memory network based Auto-Encoders (LSTM-AE) is proposed for complex aircraft system fault detection and classification, which makes use of the raw time-series data from heterogeneous sensors. The proposed method uses nominal time-series samples corresponding to healthy behavior of the system to learn a reconstruction model based on LSTM-AE framework. Then the system health index (HI) and fault feature vectors are derived from the reconstruction error matrix for fault detection and classification. The proposed method is demonstrated on a real-world data set from a commercial aircraft fleet. The typical PCV faults as well as the 390 F sensor and 450 F sensor faults due to sense line air leakage are successfully detected and distinguished based on the extracted features. The case study results show that the computed HI can effectively characterize the health state of the aircraft system and different fault types can be identified with high confidence, which is helpful for line fault troubleshooting.

Keywords: Aircraft big data; predictive maintenance; deep learning; system health monitoring; air bleed system (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:235:y:2021:i:5:p:923-940

DOI: 10.1177/1748006X211001979

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