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Online probabilistic operational safety assessment of multi-mode engineering systems using Bayesian methods

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  • Lin, Yufei
  • Chen, Maoyin
  • Zhou, Donghua

Abstract

In the past decades, engineering systems become more and more complex, and generally work at different operational modes. Since incipient fault can lead to dangerous accidents, it is crucial to develop strategies for online operational safety assessment. However, the existing online assessment methods for multi-mode engineering systems commonly assume that samples are independent, which do not hold for practical cases. This paper proposes a probabilistic framework of online operational safety assessment of multi-mode engineering systems with sample dependency. To begin with, a Gaussian mixture model (GMM) is used to characterize multiple operating modes. Then, based on the definition of safety index (SI), the SI for one single mode is calculated. At last, the Bayesian method is presented to calculate the posterior probabilities belonging to each operating mode with sample dependency. The proposed assessment strategy is applied in two examples: one is the aircraft gas turbine, another is an industrial dryer. Both examples illustrate the efficiency of the proposed method.

Suggested Citation

  • Lin, Yufei & Chen, Maoyin & Zhou, Donghua, 2013. "Online probabilistic operational safety assessment of multi-mode engineering systems using Bayesian methods," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 150-157.
    • Handle: RePEc:eee:reensy:v:119:y:2013:i:c:p:150-157
    DOI: 10.1016/j.ress.2013.05.018
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    References listed on IDEAS

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    1. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2011. "Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 925-932.
    2. Durga Rao, K. & Gopika, V. & Sanyasi Rao, V.V.S. & Kushwaha, H.S. & Verma, A.K. & Srividya, A., 2009. "Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 872-883.
    3. Podofillini, L. & Zio, E. & Mercurio, D. & Dang, V.N., 2010. "Dynamic safety assessment: Scenario identification via a possibilistic clustering approach," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 534-549.
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    Cited by:

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    2. Farcasiu, M. & Prisecaru, I., 2014. "MMOSA – A new approach of the human and organizational factor analysis in PSA," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 91-98.

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