Addressing class-imbalanced learning in real-time aero-engine gas-path fault diagnosis via feature filtering and mapping

Condition-based maintenance of aero-engines requires real-time gas-path fault diagnosis, which is crucial for reducing costs and enhancing aircraft attendance. It is imperative to ensure a low false alarm rate in fault diagnosis algorithms. Inherent variations between engines and age-related performance deterioration can lead to an increase in false alarms, necessitating online algorithm tuning. However, insufficient fault data in online tuning may lead to class imbalance and algorithmic forgetfulness of fault features, compromising effectiveness. To tackle this issue, this paper introduces a novel hybrid diagnosis algorithm that utilizes feature filtering and mapping. This method entails an engine model, a feature extractor, a feature filter, a fault classifier, and several feature mappers. The feature mappers are designed to generate fault features based on the incoming fault free data for rebalancing the tuning dataset. And the feature filter is introduced to prevent weight collapse during the online tuning. Validation through a 400-hour life cycle simulation test and a real data test indicate that the method outperforms the benchmark methods. It overcomes the class-imbalanced learning problem, showcasing its potential for aero-engine gas-path fault diagnosis.