IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2020i1p28-d466914.html
   My bibliography  Save this article

A Comprehensive Health Indicator Integrated by the Dynamic Risk Profile from Condition Monitoring Data and the Function of Financial Losses

Author

Listed:
  • Xiaoxia Liang

    (School of Engineering, London South Bank University, London SE1 0AA, UK)

  • Fang Duan

    (School of Engineering, London South Bank University, London SE1 0AA, UK)

  • Ian Bennett

    (Technology Manager Services, Shell Research Ltd., Floor 21, Shell Centre, London SE1 7NA, UK)

  • David Mba

    (Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK)

Abstract

Large rotating machinery, such as centrifugal gas compressors and pumps, have been widely applied and acted as crucial components in the oil and gas industries. Breakdowns or deteriorated performance of these rotating machines can bring significant economic loss to the companies. In order to conduct effective maintenance and avoid unplanned downtime, a system-wide health indicator is proposed in this paper. The health indicator not only uses a dynamic risk profile, but also considers financial loss and the fault probability based on condition monitoring data. This methodology is carried out by four steps: fault detection, probability of fault calculation, consequence of fault calculation and dynamic risk assessment. In our methodology, the fault probability is calculated by robust Mahalanobis distance, presenting as a system-wide feature from a sparse autoencoder fault detection model enabled early fault detection. The value of the health indicator is presented in financial loss, which assists in effective operational decision-making in a process system. To evaluate the performance of the proposed indicator, two case studies were carried out—one case tested on multivariate industrial data obtained from a pump, and another one tested on an industrial data set from a compressor. Results prove that the integrated health indicator can detect the faults at their incipient stages, indicate the degradation of the system with dynamically updated process risk at each sampling instant, and suggest an appropriate shutdown time before the system suffers severe damage. In addition, this methodology can be adapted to other machines’ health assessments, such as those of turbines and motors. The presented method of processing the industrial data set can benefit relevant readers.

Suggested Citation

  • Xiaoxia Liang & Fang Duan & Ian Bennett & David Mba, 2020. "A Comprehensive Health Indicator Integrated by the Dynamic Risk Profile from Condition Monitoring Data and the Function of Financial Losses," Energies, MDPI, vol. 14(1), pages 1-25, December.
  • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:28-:d:466914
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/1/28/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/1/28/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Eom, Yong Hwan & Yoo, Jin Woo & Hong, Sung Bin & Kim, Min Soo, 2019. "Refrigerant charge fault detection method of air source heat pump system using convolutional neural network for energy saving," Energy, Elsevier, vol. 187(C).
    2. Stanley Kaplan & B. John Garrick, 1981. "On The Quantitative Definition of Risk," Risk Analysis, John Wiley & Sons, vol. 1(1), pages 11-27, March.
    3. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2012. "Dynamic risk analysis using bow-tie approach," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 36-44.
    4. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    5. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    2. Michael Felix Pacevicius & Marilia Ramos & Davide Roverso & Christian Thun Eriksen & Nicola Paltrinieri, 2022. "Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures," Energies, MDPI, vol. 15(9), pages 1-40, April.
    3. Taleb-Berrouane, Mohammed & Khan, Faisal & Hawboldt, Kelly, 2021. "Corrosion risk assessment using adaptive bow-tie (ABT) analysis," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    4. Zheng, Xiaoyu & Tamaki, Hitoshi & Sugiyama, Tomoyuki & Maruyama, Yu, 2022. "Dynamic probabilistic risk assessment of nuclear power plants using multi-fidelity simulations," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    5. Nguyen, Son & Chen, Peggy Shu-Ling & Du, Yuquan & Thai, Vinh V., 2021. "An Operational Risk Analysis Model for Container Shipping Systems considering Uncertainty Quantification," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    6. Gundula Glowka & Andreas Kallmünzer & Anita Zehrer, 2021. "Enterprise risk management in small and medium family enterprises: the role of family involvement and CEO tenure," International Entrepreneurship and Management Journal, Springer, vol. 17(3), pages 1213-1231, September.
    7. Benischke, Mirko H. & Guldiken, Orhun & Doh, Jonathan P. & Martin, Geoffrey & Zhang, Yanze, 2022. "Towards a behavioral theory of MNC response to political risk and uncertainty: The role of CEO wealth at risk," Journal of World Business, Elsevier, vol. 57(1).
    8. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    9. Zhong, Fangliang & Calautit, John Kaiser & Wu, Yupeng, 2022. "Assessment of HVAC system operational fault impacts and multiple faults interactions under climate change," Energy, Elsevier, vol. 258(C).
    10. K. Karthikeyan & S. Bharath & K. Ranjith Kumar, 2012. "An Empirical Study on Investors’ Perception towards Mutual Fund Products through Banks with Reference to Tiruchirapalli City, Tamil Nadu," Vision, , vol. 16(2), pages 101-108, June.
    11. Nicola Paltrinieri & Nicolas Dechy & Ernesto Salzano & Mike Wardman & Valerio Cozzani, 2012. "Lessons Learned from Toulouse and Buncefield Disasters: From Risk Analysis Failures to the Identification of Atypical Scenarios Through a Better Knowledge Management," Risk Analysis, John Wiley & Sons, vol. 32(8), pages 1404-1419, August.
    12. Liu, Aihua & Chen, Ke & Huang, Xiaofei & Li, Didi & Zhang, Xiaochun, 2021. "Dynamic risk assessment model of buried gas pipelines based on system dynamics," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    13. Louis Anthony (Tony) Cox, Jr., 2012. "Community Resilience and Decision Theory Challenges for Catastrophic Events," Risk Analysis, John Wiley & Sons, vol. 32(11), pages 1919-1934, November.
    14. Chen, Fuzhong & Hsu, Chien-Lung & Lin, Arthur J. & Li, Haifeng, 2020. "Holding risky financial assets and subjective wellbeing: Empirical evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    15. Niël Almero Krüger & Natanya Meyer, 2021. "The Development of a Small and Medium-Sized Business Risk Management Intervention Tool," JRFM, MDPI, vol. 14(7), pages 1-14, July.
    16. Khastgir, Siddartha & Brewerton, Simon & Thomas, John & Jennings, Paul, 2021. "Systems Approach to Creating Test Scenarios for Automated Driving Systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    17. James H. Lambert & Rachel K. Jennings & Nilesh N. Joshi, 2006. "Integration of risk identification with business process models," Systems Engineering, John Wiley & Sons, vol. 9(3), pages 187-198, September.
    18. Johnson, Caroline A. & Flage, Roger & Guikema, Seth D., 2021. "Feasibility study of PRA for critical infrastructure risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    19. Kasai, Naoya & Matsuhashi, Shigemi & Sekine, Kazuyoshi, 2013. "Accident occurrence model for the risk analysis of industrialfacilities," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 71-74.
    20. J. C. Helton & F. J. Davis, 2002. "Illustration of Sampling‐Based Methods for Uncertainty and Sensitivity Analysis," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 591-622, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:28-:d:466914. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.