Dynamic business continuity assessment using condition monitoring data

Jinduo Xing (1) , Zhiguo Zeng (2) , Enrico Zio (2)

1 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
2 LGI - Laboratoire Génie Industriel - EA 2606

10 Concerns on the impacts of disruptive events of various nature on business operations have increased 11 significantly during the past decades. In this respect, business continuity management (BCM) has been proposed as 12 a comprehensive and proactive framework to prevent the disruptive events from impacting the business operations 13 and reduce their potential damages. Most existing business continuity assessment (BCA) models that numerically 14 quantify the business continuity are time-static, in the sense that the analysis done before operation is not updated to 15 consider the aging and degradation of components and systems which influence their vulnerability and resistance to 16 disruptive events. On the other hand, condition monitoring is more and more adopted in industry to maintain under 17 control the state of components and systems. On this basis, in this work, a dynamic and quantitative method is 18 proposed to integrate in BCA the information on the conditions of components and systems. Specifically, a particle 19 filtering-based method is developed to integrate condition monitoring data on the safety barriers installed for system 20 protection, to predict their reliability as their condition changes due to aging. An installment model and a stochastic 21 price model are also employed to quantify the time-dependent revenues and tolerable losses from operating the 22 system. A simulation model is developed to evaluate dynamic business continuity metrics originally introduced. A 23 case study regarding a nuclear power plant (NPP) risk scenario is worked out to demonstrate the applicability of the 24 proposed approach. 25

Domaines: Informatique [cs]

Jinduo Xing, Zhiguo Zeng, Enrico Zio. Dynamic business continuity assessment using condition monitoring data. International Journal of Disaster Risk Reduction, 2019, 41, pp.101334. ⟨10.1016/j.ijdrr.2019.101334⟩. ⟨hal-02428516⟩