Transient gas path fault diagnosis of aero-engine based on domain adaptive offline reinforcement learning

Xu, Jinghui; Wang, Ye; Wang, Zepeng; Wang, Xizhen; Zhao, Yongjun

doi:10.1016/j.ast.2024.109701

Aerospace Science and Technology

2024

DOI: 10.1016/j.ast.2024.109701

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Transient gas path fault diagnosis of aero-engine based on domain adaptive offline reinforcement learning

Jinghui Xu,

Ye Wang,

Zepeng Wang

et al.

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Aero-Engine Fault Detection with an LSTM Auto-Encoder Combined with a Self-Attention Mechanism

Du,

Zhang,

Meng

et al. 2024

Machines

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The safe operation of aero-engines is crucial for ensuring flight safety, and effective fault detection methods are fundamental to achieving this objective. In this paper, we propose a novel approach that integrates an auto-encoder with long short-term memory (LSTM) networks and a self-attention mechanism for the anomaly detection of aero-engine time-series data. The dataset utilized in this study was simulated from real data and injected with fault information. A fault detection model is developed utilizing normal data samples for training and faulty data samples for testing. The LSTM auto-encoder processes the time-series data through an encoder–decoder architecture, extracting latent representations and reconstructing the original inputs. Furthermore, the self-attention mechanism captures long-range dependencies and significant features within the sequences, thereby enhancing the detection accuracy of the model. Comparative analyses with the traditional LSTM auto-encoder, as well as one-class support vector machines (OC-SVM) and isolation forests (IF), reveal that the experimental results substantiate the feasibility and effectiveness of the proposed method, highlighting its potential value in engineering applications.

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Aero-Engine Fault Detection with an LSTM Auto-Encoder Combined with a Self-Attention Mechanism

Du,

Zhang,

Meng

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

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Transient gas path fault diagnosis of aero-engine based on domain adaptive offline reinforcement learning

Cited by 1 publication

References 37 publications

Aero-Engine Fault Detection with an LSTM Auto-Encoder Combined with a Self-Attention Mechanism

Aero-Engine Fault Detection with an LSTM Auto-Encoder Combined with a Self-Attention Mechanism

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