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Anomaly detection of aircraft lead‐acid battery
Author(s) -
Zhao Wenjie,
Zhang Yushu,
Zhu Ye,
Xu Peng
Publication year - 2021
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2789
Subject(s) - lead–acid battery , anomaly detection , reliability (semiconductor) , battery (electricity) , reliability engineering , civil aviation , aviation , engineering , fault detection and isolation , computer science , automotive engineering , power (physics) , aerospace engineering , electrical engineering , artificial intelligence , physics , quantum mechanics , actuator
The lead‐acid battery has been widely used in various fields. In civil aviation aircraft, it plays a vital role in the power system to maintain normal operation during the flight mission. Thus, an effective abnormal detection system for monitoring and diagnosing the status of aircraft lead‐acid battery is essential to ensure its safety and reliability. This paper aims to effectively identify aircraft battery faulty using unsupervised anomaly detection techniques. It introduces state‐of‐the‐art anomaly detection algorithms and evaluates their performance on a large real civil aviation battery data. The experimental results show that the latest isolation‐based anomaly detectors, iForest and iNNE, have outstanding performance on this task and have promising applicability as efficient methods for guaranteeing the lead‐acid battery quality and reliability in civil aviation aircraft.