Domino Effect Analysis Using Bayesian Networks (original) (raw)
Related papers
Practical Aspects of Application of Bayesian Networks to Cause and Effect Modeling in Process Safety
2018
Cause and effect scenarios in process safety are commonly modeled using fault tree analysis, event tree analysis, and/or bow tie methods. These can be readily mapped into Bayesian networks, and there have been several applications of the same. Bayesian network offers several advantages including easy visualization, updating as well as forward and backward calculations. However, there are several practical aspects that are to be kept in mind while modeling with Bayesian networks. This includes the increase in number of parent nodes and state entries in conditional probability tables, the use of equations, and difficulties in populating the same meaningfully with probability values. This paper will discuss the above factors in cause and effect modeling with Bayesian networks including the use of object-oriented Bayesian network and Noisy gates to handle the large number of parents and will be useful for researchers in the subject.
Dynamic domino effect risk assessment using Petri-nets
Process Safety and Environmental Protection, 2019
The domino effect accidents in process industries pose a severe threat to human life and the environment and have the potential to affect nearby facilities as well. Numerous techniques, such as the Bayesian network, have been used for modelling the domino effect. However, these techniques have inherent limitations. These include the inability to consider complex behaviour of process equipment in combined loading and the time dependency of equipment failure. In the current study, a Generalised Stochastic Petri-net model, called as DOMINO-GSPN, is developed to model domino effect accident likelihood and its propagation pattern. The proposed technique is capable of modelling time-dependent failure behaviour of the process component in combined loading. The results from the model are useful in monitoring process risk. A case study is used to demonstrate the application and effectiveness of the model. The results from the model are compared with the past study of a Bayesian network-based domino effect model. This comparative analysis highlights the unique feature of the model and its relevance as a domino effect risk assessment and management tool.
Probabilistic Modeling of Failure Domino Effects in Chemical Plants
2018 Eighth Latin-American Symposium on Dependable Computing (LADC), 2018
Chemical process plants are subject to risk caused by the handling and storage of hazardous substances. Major accidents may occur, particularly in those unfortunate circumstances when a triggering event produces a cascading accident that propagates to other units, a failure propagation scenario known as domino effect. An important aspect of designing such industrial plants is to properly arrange hazardous equipment such that, in the event of failures, cascading effects are minimized. In this work, we present a modeling approach to perform a probabilistic analysis of the likelihood of domino effects caused by propagating vapor cloud explosions. The approach combines the modeling of accident propagation based on physical properties of gas clouds, such as released mass and explosion distance, with the probabilistic modeling of cascading effects based on Stochastic Petri Nets. The proposed methodology is subsequently applied to a case study where different layouts of atmospheric gasoline tanks are analyzed, in order to evaluate the likelihood of domino effect occurrence.
Models for domino effect analysis in chemical process industries
Process Safety Progress, 1998
In the risk assessment parlance, especially with reference to chemical process industries, the term “domino effect” is used to denote “chain of accidents,” or situations when a fire/explosion/missile/toxic load generated by an accident in one unit in an industry causes secondary and higher order accidents in other units. The multi-accident catastrophe which occurred in a refinery at Vishakhapatnam, India, on September 14, 1997, claiming 60 lives and causing damages to property worth over Rs 600 million, is the most recent example of the damage potential of domino effect.But, even as the domino effect has been documented since 1947, very little attention has been paid towards modeling this phenomena. In this paper we have provided a conceptual framework based on sets of appropriate models to forecast domino effects, and assess their likely magnitudes and adverse impacts, while conducting risk assessment in a chemical process industry. The utilizability of the framework has been illustrated with a case study.
Safety Science, 2020
Recent catastrophic accidents in China and the USA urge and justify a thorough study on current & future research trends in the development of modeling methods and protection strategies for prevention and mitigation of large-scale escalating events or so-called domino effects in the process and chemical industries. This paper firstly provides an overview of what constitutes domino effects based on the definition and features, characterizing domino effect studies according to different research issues and approaches. The modeling approaches are grouped into three types while the protection strategies are divided into five categories, followed by detailed descriptions of representative modeling approaches and management strategies in chemical plants and clusters. The current research trends in this field are obtained based on the analysis of research work on domino effects caused by accidental events, nature events, and intentional attacks over a period of the past 30 years. A comparison analysis is conducted for the current modeling approaches and management strategies to pose their applications. Finally, this paper offers future research directions and identifies critical challenges in the field, aiming at improving the safety and security of chemical industrial areas so as to prevent and mitigate domino effects.
A Bayesian Network-Based Approach for Failure Analysis in Weapon Industry
Journal of Thermal Engineering, 2021
Gun and rifle manufacturing contain various failures in the process of CNC machining, material supply, research & development, infrastructure and, operator. Due to these failures, the enterprise is exposed to great economic losses and a decrease in competition in the global market. In addition, failures in production cause events that seriously threaten human health. Failure analysis can increase safety by determining the cause of potential errors and taking measures for identified errors in the life cycle of the products. Therefore, this study employs a Bayesian Network (BN)based modeling approach for capturing dependency among the basic events and obtaining top event probability. Firstly, a fault tree analysis (FTA) diagram is constructed, since its target is to pinpoint how basic event failures result in a top event (system) failure by an AND/OR logical gate. While, AND logical gate should take place in both cases, it is sufficient to realize one of the states in the OR logical g...
University of Iowa An extended Bayesian network approach for analyzing supply chain disruptions
Supply chain management (SCM) is the oversight of materials, information, and finances as they move in a process from supplier to manufacturer to wholesaler to retailer to consumer. Supply chain management involves coordinating and integrating these flows both within and among companies as efficiently as possible. The supply chain consists of interconnected components that can be complex and dynamic in nature. Therefore, an interruption in one subnetwork of the system may have an adverse effect on another subnetworks, which will result in a supply chain disruption.
Process Disturbance Cause & Effect Analysis Using Bayesian Networks
IFAC-PapersOnLine, 2015
Process disturbances can propagate over entire plants and it can be difficult to locate their root causes from observed effects. Bayesian Networks offer a way to represent unit operations, processes and whole plants as probabilistic models which can be used to infer and rank likely causes from observed effects. This paper presents a methodology to use deterministic steady-state process models to derive Bayesian Networks based on alarm event detection. An example heat recovery network is used to illustrate the model building and inferential procedures.
Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches
Reliability Engineering & System Safety, 2011
Safety analysis in gas process facilities is necessary to prevent unwanted events that may cause catastrophic accidents. Accident scenario analysis with probability updating is the key to dynamic safety analysis. Although conventional failure assessment techniques such as fault tree (FT) have been used effectively for this purpose, they suffer severe limitations of static structure and uncertainty handling, which are of great significance in process safety analysis. Bayesian network (BN) is an alternative technique with ample potential for application in safety analysis. BNs have a strong similarity to FTs in many respects; however, the distinct advantages making them more suitable than FTs are their ability in explicitly representing the dependencies of events, updating probabilities, and coping with uncertainties. The objective of this paper is to demonstrate the application of BNs in safety analysis of process systems. The first part of the paper shows those modeling aspects that are common between FT and BN, giving preference to BN due to its ability to update probabilities. The second part is devoted to various modeling features of BN, helping to incorporate multi-state variables, dependent failures, functional uncertainty, and expert opinion which are frequently encountered in safety analysis, but cannot be considered by FT. The paper concludes that BN is a superior technique in safety analysis because of its flexible structure, allowing it to fit a wide variety of accident scenarios.