Continual contrastive reinforcement learning: Towards stronger agent for environment-aware fault diagnosis of aero-engines through long-term optimization under highly imbalance scenarios

Abstract

Although the stability of aero-engines is high, their failures can lead to catastrophic consequences. Due to the infrequent nature of faults, traditional data-driven fault diagnosis methods rely on limited amounts of historical failure data for training classification models. They cannot update models on time in response to environmental changes and data growth. To address the issue, this paper proposes a new machine learning method, i.e., Continual Contrastive Reinforcement Learning (CCRL), that integrates environmental interaction and continual dynamic evolution for fault diagnosis of aero-engine under conditions of high imbalance and continually growing data. First, the operating environment of the airline is treated as the learning environment for the agent. The aircraft’s flight data is used as the state information provided by the environment, while the failure identification results from ground personnel and experts serve as the labels for this state information. This framework ensures the agent can continually learn in the face of increasing data volumes. Next, a contrastive learning encoder for highly imbalanced scenarios is designed, where a large number of normal samples are used to train an encoder that constructs positive and negative sample pairs with actual data, fine-tuning the encoder to improve its ability to distinguish different faults. Finally, the contrastive learning encoder is embedded into the enhanced learning model, enabling the agent to better perceive environmental changes and diagnose failures under highly imbalanced scenarios. This paper conducts a series of contrastive and ablation experiments using real data, which fully validate the application potential of the proposed method.