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Diagnostic Row Reasoning Method Based on Multiple-Valued Evaluation of Residuals and Elementary Symptoms Sequence

Author

Listed:
  • Jan Maciej Kościelny

    (Institute of Automatic Control and Robotics, Faculty of Mechatronics, Warsaw University of Technology, św. A. Boboli 8, 02-525 Warszawa, Poland)

  • Michał Syfert

    (Institute of Automatic Control and Robotics, Faculty of Mechatronics, Warsaw University of Technology, św. A. Boboli 8, 02-525 Warszawa, Poland)

  • Paweł Wnuk

    (Institute of Automatic Control and Robotics, Faculty of Mechatronics, Warsaw University of Technology, św. A. Boboli 8, 02-525 Warszawa, Poland)

Abstract

The paper analyses the research problem of conducting diagnostic reasoning for dynamic objects to eliminate the possibility of formulating false diagnoses resulting from different delays of the symptoms related to a particular fault while simultaneously striving to obtain high distinguishability. The research aimed to develop a new diagnostic inference method robust to symptom delays and characterised by high accuracy of generated diagnosis. Known methods ensuring the correctness of inference in the case of symptom delays but at the cost of reducing distinguishability of faults have been characterised. A new inference method was developed, which uses the three-valued residual evaluation and knowledge regarding elementary symptom sequences. A formal description of the diagnosing system and the proposed method are given. The method of obtaining the knowledge about the order of symptoms based on a cause-and-effect graph and was characterised. The method’s effectiveness was presented in simulation studies on the example of diagnosing a set of serially connected tanks. The comparison of the fault distinguishability obtained using the proposed method and other approaches illustrates the new method’s advantages.

Suggested Citation

  • Jan Maciej Kościelny & Michał Syfert & Paweł Wnuk, 2021. "Diagnostic Row Reasoning Method Based on Multiple-Valued Evaluation of Residuals and Elementary Symptoms Sequence," Energies, MDPI, vol. 14(9), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2476-:d:543801
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    References listed on IDEAS

    as
    1. Zhidi Lin & Dongliang Duan & Qi Yang & Xuemin Hong & Xiang Cheng & Liuqing Yang & Shuguang Cui, 2020. "Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources," Energies, MDPI, vol. 13(1), pages 1-16, January.
    2. Zhao Zhang & Xiao He, 2020. "Fault-Structure-Based Active Fault Diagnosis: A Geometric Observer Approach," Energies, MDPI, vol. 13(17), pages 1-17, August.
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    Citations

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    Cited by:

    1. Marcin Tomczyk & Ryszard Mielnik & Anna Plichta & Iwona Goldasz & Maciej Sułowicz, 2021. "Identification of Inter-Turn Short-Circuits in Induction Motor Stator Winding Using Simulated Annealing," Energies, MDPI, vol. 15(1), pages 1-19, December.
    2. Jan Maciej Kościelny & Michał Syfert & Paweł Wnuk, 2022. "Diagnostic Column Reasoning Based on Multi-Valued Evaluation of Residuals and the Elementary Symptoms Sequence," Energies, MDPI, vol. 15(7), pages 1-22, April.
    3. Michał Syfert & Andrzej Ordys & Jan Maciej Kościelny & Paweł Wnuk & Jakub Możaryn & Krzysztof Kukiełka, 2022. "Integrated Approach to Diagnostics of Failures and Cyber-Attacks in Industrial Control Systems," Energies, MDPI, vol. 15(17), pages 1-24, August.
    4. Marcin Tomczyk & Ryszard Mielnik & Anna Plichta & Iwona Gołdasz & Maciej Sułowicz, 2021. "Application of Genetic Algorithm for Inter-Turn Short Circuit Detection in Stator Winding of Induction Motor," Energies, MDPI, vol. 14(24), pages 1-20, December.

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