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A Method for Predicting Steam Turbine Generator Faults Based on MI.

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Title: A Method for Predicting Steam Turbine Generator Faults Based on MI.
Authors: Gao, Tao1 (AUTHOR), Liu, Minghao1 (AUTHOR), Ni, He1 (AUTHOR) elegance2006@sina.com
Source: Energies (19961073). Jun2026, Vol. 19 Issue 12, p2861. 23p.
Subject Terms: *Feature selection, *Fault diagnosis, *Deep learning, *Information measurement, *Electric power production, *Global optimization
Abstract: To improve the accuracy of fault prediction for power generation steam turbines and address the challenges associated with high-dimensional, nonlinear monitoring data and cumbersome hyperparameter tuning, this study proposes an intelligent fault prediction method. Although mutual information (MI)-based feature selection and Bayesian optimization (BO) for hyperparameter tuning have each demonstrated individual success in fault diagnosis applications, existing approaches predominantly treat these two critical aspects as isolated and independent procedures. This separation limits the synergistic potential between feature quality and model configuration, leaving a gap in coordinated, fully automated fault prediction frameworks for steam turbines. To bridge this gap, the proposed method, termed BO-CNN-BiLSTM, presents an automated pipeline that sequentially integrates MI-based adaptive feature selection with Bayesian optimization for hyperparameter tuning of a CNN-BiLSTM network. Initially, MI combined with K-means clustering automatically identifies and retains key features strongly associated with fault states, effectively reducing input dimensionality. Subsequently, a BO framework is employed to autonomously search for the optimal hyperparameter configuration, achieving seamless integration from feature selection to model optimization. Validation via a self-built physical-information fusion experimental platform demonstrates that the optimized model attains a root mean square error (RMSE) of 0.324 and a coefficient of determination (R2) of 0.888 on the test set. Its predictive performance significantly surpasses that of models lacking Bayesian optimization, as well as those employing standalone CNN or BiLSTM architectures. This study thus presents a highly automated, accurate, and practical intelligent fault prediction scheme for steam turbines in power generation. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194909310
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: A Method for Predicting Steam Turbine Generator Faults Based on MI.
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  Data: <searchLink fieldCode="AR" term="%22Gao%2C+Tao%22">Gao, Tao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Minghao%22">Liu, Minghao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ni%2C+He%22">Ni, He</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> elegance2006@sina.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 12, p2861. 23p.
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  Data: *<searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br />*<searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Information+measurement%22">Information measurement</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+production%22">Electric power production</searchLink><br />*<searchLink fieldCode="DE" term="%22Global+optimization%22">Global optimization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To improve the accuracy of fault prediction for power generation steam turbines and address the challenges associated with high-dimensional, nonlinear monitoring data and cumbersome hyperparameter tuning, this study proposes an intelligent fault prediction method. Although mutual information (MI)-based feature selection and Bayesian optimization (BO) for hyperparameter tuning have each demonstrated individual success in fault diagnosis applications, existing approaches predominantly treat these two critical aspects as isolated and independent procedures. This separation limits the synergistic potential between feature quality and model configuration, leaving a gap in coordinated, fully automated fault prediction frameworks for steam turbines. To bridge this gap, the proposed method, termed BO-CNN-BiLSTM, presents an automated pipeline that sequentially integrates MI-based adaptive feature selection with Bayesian optimization for hyperparameter tuning of a CNN-BiLSTM network. Initially, MI combined with K-means clustering automatically identifies and retains key features strongly associated with fault states, effectively reducing input dimensionality. Subsequently, a BO framework is employed to autonomously search for the optimal hyperparameter configuration, achieving seamless integration from feature selection to model optimization. Validation via a self-built physical-information fusion experimental platform demonstrates that the optimized model attains a root mean square error (RMSE) of 0.324 and a coefficient of determination (R2) of 0.888 on the test set. Its predictive performance significantly surpasses that of models lacking Bayesian optimization, as well as those employing standalone CNN or BiLSTM architectures. This study thus presents a highly automated, accurate, and practical intelligent fault prediction scheme for steam turbines in power generation. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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        Value: 10.3390/en19122861
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      – Code: eng
        Text: English
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        PageCount: 23
        StartPage: 2861
    Subjects:
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Information measurement
        Type: general
      – SubjectFull: Electric power production
        Type: general
      – SubjectFull: Global optimization
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      – TitleFull: A Method for Predicting Steam Turbine Generator Faults Based on MI.
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            – D: 15
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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