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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Risk-Based Fault Detection Using Bayesian Networks Based on Failure Mode and Effect Analysis.

Bálint Levente Tarcsay1, Ágnes Bárkányi1, Sándor Németh1

  • 1Department of Process Engineering, University of Pannonia, 8200 Veszprém, Hungary.

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|June 19, 2024
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Summary
This summary is machine-generated.

This study introduces a hybrid fault detection (FD) method using dynamic principal component analysis (DPCA) and failure mode and effects analysis (FMEA) Bayesian networks (BNs). This approach enhances industrial safety by assessing process fault risks to minimize false alarms.

Keywords:
Bayesian networksDPCAFMEAdynamic risk assessmentfault detection

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Area of Science:

  • Chemical Engineering
  • Process Safety
  • Industrial Monitoring

Background:

  • Industrial fault detection (FD) methods often lack robust integration of process risk assessment.
  • Minimizing alarm rates requires differentiating between safety-critical and non-safety-critical process abnormalities.
  • Existing FD techniques offer limited capabilities for incorporating dynamic risk analysis.

Purpose of the Study:

  • To introduce a novel hybrid risk-based fault detection (FD) method.
  • To integrate dynamic principal component analysis (DPCA) with failure mode and effects analysis (FMEA) based Bayesian networks (BNs).
  • To improve the accuracy of fault detection by assessing process fault risks and minimizing alarm rates.

Main Methods:

  • Developed a hybrid model combining DPCA and FMEA-based BNs.
  • Utilized FMEA to construct a BN for a supervised process.
  • Employed DPCA to analyze process data and estimate modified risk priority numbers (RPNs).

Main Results:

  • The proposed hybrid method effectively estimates modified RPNs for different process states.
  • Successfully differentiated between process abnormalities by incorporating both BN and DPCA results.
  • Demonstrated the method's efficacy on an industrial benchmark and a liquid organic hydrogen carrier (LOHC) reactor model.

Conclusions:

  • The hybrid DPCA-FMEA-BN approach provides a robust framework for risk-based fault detection.
  • This method enhances industrial safety by accurately identifying critical process faults.
  • The technique is applicable to complex industrial processes, including emerging technologies like LOHC.