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

• DOI: 10.1016/j.cja.2018.03.024
• Publication Date: 2018-04-13T00:48:08Z
• Authors: Jianzhong SUN; Chaoyi LI; Cui LIU; Ziwei GONG; Ronghui WANG
• Keywords: Prognostics; Condition Monitoring; Aircraft Maintenance; Airplane; Health indicator; State of health

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 challenges for airborne system health monitoring is to find an appropriate indicator that highly related actual degradation state system. This paper proposed novel extraction method based available sensor parameters Air Conditioning System (ACS) model. Firstly, specific Airplane Condition Monitoring (ACMS) report ACS defined. Then non-parametric modeling technique adopted calculate raw ACMS data. The validated single-aisle widely used short medium-haul routes, using more than 6000 reports collected from fleet during year. case study result shows can effectively characterize ACS, which provide valuable information proactive maintenance plan advance.