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Complex system anomaly detection via learnable temporal-spatial graph with degradation tendency segmentation.

Qinfeng Han1, Jinglong Chen1, Jun Wang2

  • 1State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China.

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|July 10, 2024
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Summary
This summary is machine-generated.

This study introduces a new framework for system-level anomaly detection (AD) in equipment like liquid rocket engines (LREs). The method improves safety by better identifying system anomalies using prior knowledge and temporal-spatial dependencies.

Keywords:
Anomaly detectionEncoder-decoderLiquid rocket engineMultivariate time seriesSample segmentation

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Area of Science:

  • Engineering
  • Data Science
  • Mechanical Systems

Background:

  • System-level anomaly detection (AD) is vital for equipment safety and reliability, particularly in complex systems like liquid rocket engines (LREs).
  • Existing AD methods often overlook crucial prior knowledge of mechanical systems and fail to adequately capture the unique characteristics of system-level anomalies, differing from component faults.
  • Current approaches struggle to tightly integrate observational data with inherent data relations and address the weakness and non-independence of system-level anomalies.

Purpose of the Study:

  • To propose a novel separate reconstruction framework for system-level anomaly detection (AD) that overcomes the limitations of current methods.
  • To enhance the learning of normal features by preventing attenuation of anomalous features through sample division and maximizing distribution discrepancies.
  • To effectively model complex temporal-spatial dependencies and integrate both prior knowledge and data characteristics for robust AD.

Main Methods:

  • A separate reconstruction framework is proposed, dividing single samples into two temporal segments to prevent anomalous feature attenuation.
  • The Mean Maximum Discrepancy (MMD) is maximized between feature segments to encourage encoders to learn normal features with distinct distributions.
  • Temporal convolution and graph attention are employed to model temporal-spatial dependencies, complemented by a joint graph learning strategy for integrating prior knowledge and data characteristics.

Main Results:

  • The proposed method was evaluated on two real-world multi-sensor datasets from liquid rocket engine (LRE) operations.
  • The results demonstrated the effectiveness of the separate reconstruction framework in identifying system-level anomalies.
  • The approach showed significant potential for improving the safety and reliability of equipment through advanced anomaly detection.

Conclusions:

  • The developed separate reconstruction framework offers a promising approach for system-level anomaly detection (AD).
  • Integrating prior mechanical knowledge with data-driven temporal-spatial modeling enhances AD performance.
  • The method shows significant potential for ensuring the safety and reliability of critical equipment like LREs.