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Sensor Fault Diagnosis Using a Machine Fuzzy Lyapunov-Based Computed Ratio Algorithm.

Shahnaz TayebiHaghighi1, Insoo Koo1

  • 1Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 680-749, Korea.

Sensors (Basel, Switzerland)
|April 23, 2022
PubMed
Summary

This study introduces a novel fuzzy Lyapunov-based computed ratio observer with support vector machine (SVM) for internal combustion engine (ICE) sensor anomaly identification. The advanced method achieves 98.17% accuracy in classifying sensor faults, significantly outperforming existing techniques.

Keywords:
Gaussian autoregressive methodLyapunov robust methodcomputed ratio observerfuzzy approachinternal combustion enginesensor anomaly detectionsupport vector machine

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Area of Science:

  • Engineering
  • Computer Science

Background:

  • Anomaly identification in internal combustion engine (ICE) sensors is crucial for operational efficiency and safety.
  • Existing methods for sensor fault detection face limitations in accuracy and robustness.

Purpose of the Study:

  • To develop and evaluate a novel indirect fuzzy Lyapunov-based computed ratio observer integrated with a support vector machine (SVM) for enhanced ICE sensor fault classification.
  • To improve the accuracy and reliability of sensor anomaly detection in internal combustion engines.

Main Methods:

  • A three-layer approach was implemented: signal preprocessing (RMS signal extraction), observation (fuzzy Lyapunov-based computed ratio observer with Gaussian autoregressive-Laguerre modeling), and residual generation/classification (SVM).
  • The observer estimates the fuel-to-air-ratio signal, and anomalies are detected by comparing original and estimated signals.
  • Support vector machine (SVM) is utilized for classifying the generated residual signals.

Main Results:

  • The proposed fuzzy Lyapunov-based computed ratio observer achieved a high accuracy of 98.17% in sensor anomaly classification.
  • The method demonstrated significant improvements in fault classification accuracy compared to conventional approaches, including computed ratio observer and Kalman filter techniques.
  • Specific accuracy improvements were noted: 8.37% over computed ratio observer, 2.17% over Lyapunov-based computed ratio observer, 6.17% over fuzzy feedback linearization, 4.57% over self-tuning fuzzy robust multi-integral observer, and 5.37% over Kalman filter.

Conclusions:

  • The integrated fuzzy Lyapunov-based computed ratio observer and SVM provide a highly accurate and effective solution for ICE sensor anomaly identification.
  • This advanced technique offers superior performance over existing methods, paving the way for more reliable engine monitoring systems.