Health Monitoring of Landing Gear Retraction/Extension System Based on Optimized Fuzzy C-Means Algorithm

• Published in: IEEE access Vol. 8; pp. 219611 - 219621
• Main Authors: Chen, Jie, Chen, Senyao, Liu, Zhenbao, Luo, Caikun, Jing, Zhengdong, Xu, Qingshan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Aircraft; Aircraft accidents & safety; Aircraft landing; Aircraft safety; Algorithms; Cluster analysis; Clustering; Clustering algorithms; Decision making; Diagnostic systems; fuzzy c-means; Gears; health monitoring; Hydraulic systems; Landing gear; Landing gear retraction/extension system; Lubricating oils; MDS; Monitoring
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3042888

The landing gear retraction/extension(R/E) system has a critical impact on the safety of aircraft's take-off and landing, and its health status is important to decision-making of the system's prognostics and health management (PHM). With flight parameter data, a method to monitor the health of aircraft landing gear R/E system is proposed based on the improved fuzzy c-means algorithm (FCM). The landing gear health status are classified by the FCM cluster, and the granularity principle and density function are utilized to optimize the number of FCM algorithm clusters and the initial clustering center to better classification. At the same time, an optimized multi-dimensional scaling algorithm (MDS) is used to obtain low-dimensional features which facilitate cluster analysis. And the algorithm comparison and case analysis results show that the proposed method has a great health monitoring effect.