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Fault detection and identification method using observer-based residuals

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Listed:
  • Jeong, Haedong
  • Park, Bumsoo
  • Park, Seungtae
  • Min, Hyungcheol
  • Lee, Seungchul

Abstract

Manufacturing machinery is becoming increasingly complicated, and machinery breakdowns not only reduce efficiency, but also pose safety hazards. Due to the needs for maintaining high reliability within facility operation, various methods for condition monitoring are suggested as the importance of maintenance has increased. Among the various prognostics and health management (PHM) techniques, this paper introduces a model-based fault detection and isolation (FDI) technique for the diagnosis of machine health conditions. The proposed approach identifies faults by extracting fault signal information such as the magnitude or shape of the fault based on a defined relationship between a fault signal and observer theory. To validate the proposed method, a numerical simulation is conducted to demonstrate its fault detection and identification capabilities in various situations. The proposed method and data-driven methods are then compared with regard to their fault diagnosis performance.

Suggested Citation

  • Jeong, Haedong & Park, Bumsoo & Park, Seungtae & Min, Hyungcheol & Lee, Seungchul, 2019. "Fault detection and identification method using observer-based residuals," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 27-40.
    • Handle: RePEc:eee:reensy:v:184:y:2019:i:c:p:27-40
    DOI: 10.1016/j.ress.2018.02.007
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    References listed on IDEAS

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    1. Lapira, Edzel & Brisset, Dustin & Davari Ardakani, Hossein & Siegel, David & Lee, Jay, 2012. "Wind turbine performance assessment using multi-regime modeling approach," Renewable Energy, Elsevier, vol. 45(C), pages 86-95.
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    Cited by:

    1. Quintanilha, Igor M. & Elias, Vitor R.M. & da Silva, Felipe B. & Fonini, Pedro A.M. & da Silva, Eduardo A.B. & Netto, Sergio L. & Apolinário, José A. & de Campos, Marcello L.R. & Martins, Wallace A., 2021. "A fault detector/classifier for closed-ring power generators using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    2. Amirhossein Hosseinzadeh Dadash & Niclas Björsell, 2024. "Infinite-Horizon Degradation Control Based on Optimization of Degradation-Aware Cost Function," Mathematics, MDPI, vol. 12(5), pages 1-24, February.
    3. Zhou, Han & Yin, Hongpeng & Chai, Yi, 2023. "Multi-grained mode partition and robust fault diagnosis for multimode industrial processes," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Zhang, Liangwei & Lin, Jing & Shao, Haidong & Zhang, Zhicong & Yan, Xiaohui & Long, Jianyu, 2021. "End-to-end unsupervised fault detection using a flow-based model," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Guo, Kai & Ye, Zhisheng & Liu, Datong & Peng, Xiyuan, 2021. "UAV flight control sensing enhancement with a data-driven adaptive fusion model," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. Saeed, Umer & Jan, Sana Ullah & Lee, Young-Doo & Koo, Insoo, 2021. "Fault diagnosis based on extremely randomized trees in wireless sensor networks," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    7. Rivas, Andy & Delipei, Gregory Kyriakos & Davis, Ian & Bhongale, Satyan & Yang, Jinan & Hou, Jason, 2024. "A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation," Reliability Engineering and System Safety, Elsevier, vol. 247(C).

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