A data-driven health indicator extraction method for aircraft air conditioning system health monitoring

作     者：Jianzhong SUN Chaoyi LI Cui LIU Ziwei GONG Ronghui WANG 

作者机构：Department of Civil Aviation Nanjing University of Aeronautics and Astronautics Maintenance Engineering DepartmentXiamen Airlines 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2019年第32卷第2期

页      面：409-416页

核心收录：

学科分类：08[工学] 082504[工学-人机与环境工程] 082503[工学-航空宇航制造工程] 0825[工学-航空宇航科学与技术] 

基　　金：supported by the National Natural Science Foundation of China (61403198) the Jiangsu Province Natural Science Foundation of China (BK20140827) China Postdoctoral Science Foundation (2015M581792) 

主　　题：Air conditioning system Aircraft health monitoring Airplane condition monitoring system Health indicator Prognostics and health management 

摘      要：Prognostics and Health Management(PHM) has become a very important tool in modern commercial aircraft. Considering limited built-in sensing devices on the legacy aircraft model,one of the challenges for airborne system health monitoring is to find an appropriate health indicator that is highly related to the actual degradation state of the system. This paper proposed a novel health indicator extraction method based on the available sensor parameters for the health monitoring of Air Conditioning System(ACS) of a legacy commercial aircraft model. Firstly, a specific Airplane Condition Monitoring System(ACMS) report for ACS health monitoring is defined. Then a non-parametric modeling technique is adopted to calculate the health indicator based on the raw ACMS report data. The proposed method is validated on a single-aisle commercial aircraft widely used for short and medium-haul routes, using more than 6000 ACMS reports collected from a fleet of aircraft during one year. The case study result shows that the proposed health indicator can effectively characterize the degradation state of the ACS, which can provide valuable information for proactive maintenance plan in advance.