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Purpose <p>Condition monitoring is critical for intelligent equipment maintenance, enabling early fault detection, improved safety, and reduced unplanned downtime. However, existing approaches are highly dependent on data quality and often lack generalization across diverse machine types and operating conditions, leading to inaccurate fault alarms.</p> Methods <p>To address these limitations, this study proposes a generalized similarity-based method for fault identification in rotating machines. The method integrates wavelet packet Bayesian thresholding to suppress noise in multidimensional data and employs the Manhattan distance metric within the generalized Enhanced Auto-Associative Kernel Regression (EAKR) model to evaluate sample similarity. A practical procedure for implementing EAKR in automatic early fault detection is also developed.</p> Results <p>An ablation study demonstrates that the proposed EAKR model achieves higher fault identification accuracy and robustness compared to traditional approaches. Furthermore, validation on real-world datasets from gas turbines, wind turbines, and centrifugal compressors confirms its broad applicability.</p> Conclusion <p>The proposed EAKR model exhibits strong generalization capability and feasibility, highlighting its potential as a fundamental approach for improving condition monitoring and enhancing the reliability of industrial rotating machinery.</p>

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EAKR: A Fundamental Model for Fault Alarming of Rotating Machinery

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • Jigang Meng,
  • Huaiyu Hui,
  • Kexin Zhang,
  • Huize Chen,
  • Wenhao Yuan,
  • Xueyu Cheng,
  • Xiaomo Jiang

摘要

Purpose

Condition monitoring is critical for intelligent equipment maintenance, enabling early fault detection, improved safety, and reduced unplanned downtime. However, existing approaches are highly dependent on data quality and often lack generalization across diverse machine types and operating conditions, leading to inaccurate fault alarms.

Methods

To address these limitations, this study proposes a generalized similarity-based method for fault identification in rotating machines. The method integrates wavelet packet Bayesian thresholding to suppress noise in multidimensional data and employs the Manhattan distance metric within the generalized Enhanced Auto-Associative Kernel Regression (EAKR) model to evaluate sample similarity. A practical procedure for implementing EAKR in automatic early fault detection is also developed.

Results

An ablation study demonstrates that the proposed EAKR model achieves higher fault identification accuracy and robustness compared to traditional approaches. Furthermore, validation on real-world datasets from gas turbines, wind turbines, and centrifugal compressors confirms its broad applicability.

Conclusion

The proposed EAKR model exhibits strong generalization capability and feasibility, highlighting its potential as a fundamental approach for improving condition monitoring and enhancing the reliability of industrial rotating machinery.