Complex engineered system health indexes extraction using low frequency raw time-series data based on deep learning methods

Data analysis methods based on deep learning are attracting more and more attention in the field of health monitoring, fault diagnosis and failure prognostics of complex systems, such as aircraft airborne systems and engines. In this study, several health monitoring methods proposed from deep learning are demonstrated based on a real data set from an airborne system of commercial aircraft, where Health Indexes (HIs) are derived based on the raw sensor data to characterize the health state of the system in-service. Determining the optimal degradation evaluation index is the key to further failure prognostics. So, a set of metrics to characterize the suitability of different of HI deriving methods has been proposed. This metrics includes monotonicity, prognosability, and trendability. The better HI selected can effectively characterize the health state of aircraft air conditioning system, which is helpful for further failure prognostics and converting the scheduled maintenance into condition-based maintenance. (C) 2020 Elsevier Ltd. All rights reserved.