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Fault-Structure-Based Active Fault Diagnosis: A Geometric Observer Approach

Author

Listed:
  • Zhao Zhang

    (Department of Automation, Tsinghua University, Beijing 100084, China)

  • Xiao He

    (Department of Automation, Tsinghua University, Beijing 100084, China)

Abstract

Fault diagnosis techniques can be classified into passive and active types. Passive approaches only utilize the original input and output signals of the system. Because of the small amplitudes, the characteristics of incipient faults are not fully represented in the data of the system, so it is difficult to detect incipient faults by passive fault diagnosis techniques. In contrast, active methods can design auxiliary signals for specific faults and inject them into the system to improve fault diagnosis performance. Therefore, active fault diagnosis techniques are utilized in this article to detect and isolate incipient faults based on the fault structure. A new framework based on observer approach for active fault diagnosis is proposed and the geometric approach based fault diagnosis observer is introduced to active fault diagnosis for the first time. Based on the dynamic equations of residuals, auxiliary signals are designed to enhance the diagnosis performance for incipient faults that have specific structures. In addition, the requirements that auxiliary signals need to meet are discussed. The proposed method can realize the seamless combination of active fault diagnosis and passive fault diagnosis. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach, and it is indicated that the proposed method significantly improves the accuracy of the diagnosis for incipient faults.

Suggested Citation

  • Zhao Zhang & Xiao He, 2020. "Fault-Structure-Based Active Fault Diagnosis: A Geometric Observer Approach," Energies, MDPI, vol. 13(17), pages 1-17, August.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4475-:d:406479
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    References listed on IDEAS

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    1. Hong Wang & Hongbin Wang & Guoqian Jiang & Jimeng Li & Yueling Wang, 2019. "Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling," Energies, MDPI, vol. 12(6), pages 1-22, March.
    2. Farzin Piltan & Cheol-Hong Kim & Jong-Myon Kim, 2019. "Advanced Adaptive Fault Diagnosis and Tolerant Control for Robot Manipulators," Energies, MDPI, vol. 12(7), pages 1-22, April.
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    Cited by:

    1. 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.

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