Having a robust health management and diagnostic strategy is an important part of a system’s operational life cycle as it can be used to detect anomalies, analyze faults/failures and predict the remaining useful life of components. By utilizing condition data and on-site feedback, data models can be trained using machine learning and statistical concepts. Once trained, the logic for data processing can be embedded on onboard controllers whilst enabling real-time health assessment and analysis. More recently, deep learning has gained increasing attention due to its potential advantages with data classification and feature extraction problems. It is an evolving research area and hence its use for aerospace maintenance applications must be researched if it can be used to increase overall system resilience or potential cost benefits for maintenance, repair, and overhaul activities. This paper focuses on investigating the application of deep learning for system health management, therefore incorporating reliable redundancy at the adequate point in the system. Deep learning is discussed, some recent developments are reviewed to clarify potential applications, after which some research issues relating to their realization are highlighted.
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