Systematic treatment of circular logics in a fault tree analysis

Modelling vicious networks with P-graph causality maps

P-graph causality maps were recently proposed as a methodology for systematic analysis of intertwined causal chains forming network-like structures. This approach uses the bipartite representation of P-graph to distinguish system components ("objects" represented by O-type nodes) from the functions they perform ("mechanisms" represented by M-type nodes). The P-graph causality map methodology was originally applied for determining structurally feasible causal networks to enable a desirable outcome to be achieved. In this work, the P-graph causality map methodology is extended to the analysis of vicious networks (i.e., causal networks with adverse outcomes). The maximal structure generation algorithm is first used to assemble the problem elements into a complete causal network; the solution structure generation algorithm is then used to enumerate all structurally feasible causal networks. Such comprehensive analysis gives insights on how to deactivate vicious networks through the removal of keystone objects and mechanisms. The extended methodology is illustrated with an ex post analysis of the 1984 Bhopal industrial disaster. Prospects for other applications to sustainability issues are also discussed.

Method based on directed graph and binary decision diagram to break logic loops

In the probabilistic safety analysis (PSA) of nuclear power plants (NPP), fault tree analysis is often taken as a useful way for reliability evaluation. Logic loops due to mutual dependencies among systems may emerge in fault trees. Logic loops or circular logics can make it complex to get the minimum cut sets. This paper presents a method based on directed graphs and Binary Decision Diagram (BDD) to break logic loops and get the minimum cut sets consistent to previous attempting methods. The paper illustrates this method by giving one linear actual case in nuclear field and non-linear case presented by Vaurio. Utilizing directed graphs and Binary Decision Diagram is effortless for reliability analysts and concerned people in a nuclear field to have an intuitive comprehension for correlations among systems without logic loops. What’s more, directed graphs and BDD can express the minimum cut sets of systems that can be understood intuitively. In addition, this present method is able to solve non-linear logic loops that is hard to operate in fault tree analysis. This proposed non-iterative method is also helpful to design good reliability engineering systems.

A graphical method for breaking logical loops based on multi-tree structure

Logical loops or circular logics, interpreted as circular supporting relations among systems, remain a longstanding challenge in the probabilistic safety assessment (PSA). Logical loops are commonly found in complex industrial systems. Due to the existence of the logical loops, the minimal cut sets cannot be directly obtained. In order to solve this problem, the logical loops should be broken properly. This paper proposes a graphical method based on multi-tree structure. By constructing the simplified multi-tree, logical loops both in linearly and non-linearly interrelated systems are solved. To illustrate this method, examples of linearly interrelated systems and non-linearly interrelated systems are given in this paper. As a supplement, this method is applied to the well-known complex logical loops in the nuclear power plant. It shows that this method is highly intuitive and efficient by means of graphs.

Asset maintenance optimisation approaches in the chemical and process industries - A review

The operational performance of a chemical process plant highly depends on the assets' condition and maintenance practices. As chemical processes are highly complex systems, increasing the risk frequencies and their interactions, the maintenance planning becomes crucial for stable operation. This paper provides a critical analysis of the recently developed approaches for asset maintenance approaches in the chemical industry. The strategies include corrective maintenance, time-based, risk-based, condition-based and opportunistic maintenance. Various methods on selecting the optimal maintenance strategy are discussed as well. This paper also evaluates reliability issues in chemical plants and integrated sites encompassing the maintenance optimisation. Several directions for potential future improvements are proposed based on this analysis, as follows: (i) potential study of exploiting production or other opportunities to postpone or conduct earlier maintenance; (ii) joint optimisation of spare part ordering strategy and data-driven maintenance planning study is needed; (iii) fault propagation modelling of structural dependent units to facilitate proper maintenance planning; (iv) a framework or tool that consider quantitative and qualitative time-variant data inputs is lacking for business-informed asset maintenance.