4.7 Article

DBN based failure prognosis method considering the response of protective layers for the complex industrial systems

Journal

ENGINEERING FAILURE ANALYSIS
Volume 79, Issue -, Pages 504-519

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engfailanal.2017.04.015

Keywords

Failure prognosis; Protective layers; Dynamic Bayesian network; Complex industrial system

Funding

  1. Natural Science Foundation of China [51574263]
  2. Science Foundation of China University of Petroleum [2462015YQ0403, C201602]
  3. Sinopec scientific research and technological development project [P14004]

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In complex industrial systems, operating, regulating, maintenance activities and external incidents take place dynamically and multiple entities in same or different subsystems interact in a complex manner. Most of single faults have multiple propagation paths. Any local slight deviation is able to propagate, spread, accumulate and increase through system fault causal chains. It will finally result in system failure and unplanned outages or even catastrophic accidents. The key issues focus on both of how to reduce the probability of fault occurrence and decrease the loss of fault consequence. The implementation of such requirements can be studied in terms of the determination of the fault root causes, prediction of the possible consequence, and also estimation of the risk and timing of various maintenance activities, which are considered in a failure prognosis scheme. This study proposes a DBN based failure prognosis method for complex system. Not only the interaction between components, but also the influence of the layers of protection in the system is considered when the dynamic failure scenarios are analyzed. Therefore the proposed method considers multiple factors including degradation mechanism, parameter deviation, the response of the layers of protection and also the external environment. With this model, the dynamic influence diagram of the components' degradation trends can be calculated and be used to evaluate the different effects of the layers of protection quantitatively. Some key problems are also explained such as how to determine the new nodes in DBN representing the behavior of protective layers and how to update the CPT in the extended model. In the case study, the proposed method is tested on the flue gas energy recovery system (FGERS) which is widely used in the petrochemical industry to demonstrate its effectiveness. It makes a great help for early warning and optimization of the layers of protection in the complex industrial system.

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