• Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

      Kabir, Sohag; Papadopoulos, Y. (2019-06)
      System safety, reliability and risk analysis are important tasks that are performed throughout the system lifecycle to ensure the dependability of safety-critical systems. Probabilistic risk assessment (PRA) approaches are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). Growing complexity of modern systems and their capability of behaving dynamically make it challenging for classical PRA techniques to analyse such systems accurately. For a comprehensive and accurate analysis of complex systems, different characteristics such as functional dependencies among components, temporal behaviour of systems, multiple failure modes/states for components/systems, and uncertainty in system behaviour and failure data are needed to be considered. Unfortunately, classical approaches are not capable of accounting for these aspects. Bayesian networks (BNs) have gained popularity in risk assessment applications due to their flexible structure and capability of incorporating most of the above mentioned aspects during analysis. Furthermore, BNs have the ability to perform diagnostic analysis. Petri Nets are another formal graphical and mathematical tool capable of modelling and analysing dynamic behaviour of systems. They are also increasingly used for system safety, reliability and risk evaluation. This paper presents a review of the applications of Bayesian networks and Petri nets in system safety, reliability and risk assessments. The review highlights the potential usefulness of the BN and PN based approaches over other classical approaches, and relative strengths and weaknesses in different practical application scenarios.
    • A Bayesian network based study on determining the relationship between job stress and safety climate factors in occurrence of accidents.

      Khoshakhlagh, A.H.; Yazdanirad, S.; Kashani, M.M.; Khatooni, E.; Hatamnegad, Y.; Kabir, Sohag (2021-12-07)
      Job stress and safety climate have been recognized as two crucial factors that can increase the risk of occupational accidents. This study was performed to determine the relationship between job stress and safety climate factors in the occurrence of accidents using the Bayesian network model. This cross-sectional study was performed on 1530 male workers of Asaluyeh petrochemical company in Iran. The participants were asked to complete the questionnaires, including demographical information and accident history questionnaire, NIOSH generic job stress questionnaire, and Nordic safety climate questionnaire. Also, work experience and the accident history data were inquired from the petrochemical health unit. Finally, the relationships between the variables were investigated using the Bayesian network model. A high job stress condition could decrease the high safety climate from 53 to 37% and increase the accident occurrence from 72 to 94%. Moreover, a low safety climate condition could increase the accident occurrence from 72 to 93%. Also, the concurrent high job stress and low safety climate could raise the accident occurrence from 72 to 93%. Among the associations between the job stress factor and safety climate dimensions, the job stress and worker's safety priority and risk non-acceptance (0.19) had the highest mean influence value. The adverse effect of high job stress conditions on accident occurrence is twofold. It can directly increase the accident occurrence probability and in another way, it can indirectly increase the accident occurrence probability by causing the safety climate to go to a lower level.
    • Computational intelligence for safety assurance of cooperative systems of systems

      Kabir, Sohag; Papadopoulos, Y. (2020-12)
      Cooperative Systems of Systems (CSoS) including Autonomous systems (AS), such as autonomous cars and related smart traffic infrastructures form a new technological frontier for their enormous economic and societal potentials in various domains. CSoS are often safety-critical systems, therefore, they are expected to have a high level of dependability. Due to the open and adaptive nature of the CSoS, the conventional methods used to provide safety assurance for traditional systems cannot be applied directly to these systems. Potential configurations and scenarios during the evolving operation are infinite and cannot be exhaustively analysed to provide guarantees a priori. This paper presents a novel framework for dynamic safety assurance of CSoS, which integrates design time models and runtime techniques to provide continuous assurance for a CSoS and its systems during operation.
    • A Conceptual Framework to Incorporate Complex Basic Events in HiP-HOPS

      Kabir, Sohag; Aslansefat, K.; Sorokos, I.; Papadopoulos, Y.; Gheraibia, Y. (Springer, 2019-10-16)
      Reliability evaluation for ensuring the uninterrupted system operation is an integral part of dependable system development. Model-based safety analysis (MBSA) techniques such as Hierarchically Performed Hazard Origin and Propagation Studies (HiP-HOPS) have made the reliability analysis process less expensive in terms of effort and time required. HiP-HOPS uses an analytical modelling approach for Fault tree analysis to automate the reliability analysis process, where each system component is associated with its failure rate or failure probability. However, such non-state-space analysis models are not capable of modelling more complex failure behaviour of component like failure/repair dependencies, e.g., spares, shared repair, imperfect coverage, etc. State-space based paradigms like Markov chain can model complex failure behaviour, but their use can lead to state-space explosion, thus undermining the overall analysis capacity. Therefore, to maintain the benefits of MBSA while not compromising on modelling capability, in this paper, we propose a conceptual framework to incorporate complex basic events in HiP-HOPS. The idea is demonstrated via an illustrative example.
    • A Conceptual Framework to Incorporate Complex Basic Events in HiP-HOPS

      Kabir, Sohag; Aslansefat, K.; Sorokos, I.; Papadopoulos, Y.; Gheraibia, Y. (2019-10-11)
      Reliability evaluation for ensuring the uninterrupted system operation is an integral part of dependable system development. Model-based safety analysis (MBSA) techniques such as Hierarchically Performed Hazard Origin and Propagation Studies (HiP-HOPS) have made the reliability analysis process less expensive in terms of effort and time required. HiP-HOPS uses an analytical modelling approach for Fault tree analysis to automate the reliability analysis process, where each system component is associated with its failure rate or failure probability. However, such non-state-space analysis models are not capable of modelling more complex failure behaviour of component like failure/repair dependencies, e.g., spares, shared repair, imperfect coverage, etc. State-space based paradigms like Markov chain can model complex failure behaviour, but their use can lead to state-space explosion, thus undermining the overall analysis capacity. Therefore, to maintain the benefits of MBSA while not compromising on modelling capability, in this paper, we propose a conceptual framework to incorporate complex basic events in HiP-HOPS. The idea is demonstrated via an illustrative example.
    • DDI: A Novel Technology And Innovation Model for Dependable, Collaborative and Autonomous Systems

      Armengaud, E.; Schneider, D.; Reich, J.; Sorokos, I.; Papadopoulos, Y.; Zeller, M.; Regan, G.; Macher, G.; Veledar, O.; Thalmann, S.; et al. (2021-02)
      Digital transformation fundamentally changes established practices in public and private sector. Hence, it represents an opportunity to improve the value creation processes (e.g., “industry 4.0”) and to rethink how to address customers’ needs such as “data-driven business models” and “Mobility-as-a-Service”. Dependable, collaborative and autono-mous systems are playing a central role in this transformation process. Furthermore, the emergence of data-driven approaches combined with autonomous systems will lead to new business models and market dynamics. Innovative approaches to re-organise the value creation ecosystem, to enable distributed engineering of dependable systems and to answer urgent questions such as liability will be required. Consequently, digital transformation requires a comprehensive multi-stakeholder approach which properly balances technology, ecosystem and business innovation. Targets of this paper are (a) to introduce digital transformation and the role of / opportunities provided by autonomous systems, (b) to introduce Digital Depednability Identities (DDI) - a technology for dependability engineering of collaborative, autonomous CPS, and (c) to propose an appropriate agile approach for innovation management based on business model innovation and co-entrepreneurship.
    • Dynamic Fault Tree Analysis: State-of-the-Art in Modeling, Analysis, and Tools

      Aslansefat, K.; Kabir, Sohag; Gheraibia, Y.; Papadopoulos, Y. (2020-06)
      Safety and reliability are two important aspects of dependability that are needed to be rigorously evaluated throughout the development life-cycle of a system. Over the years, several methodologies have been developed for the analysis of failure behavior of systems. Fault tree analysis (FTA) is one of the well-established and widely used methods for safety and reliability engineering of systems. Fault tree, in its classical static form, is inadequate for modeling dynamic interactions between components and is unable to include temporal and statistical dependencies in the model. Several attempts have been made to alleviate the aforementioned limitations of static fault trees (SFT). Dynamic fault trees (DFT) were introduced to enhance the modeling power of its static counterpart. In DFT, the expressiveness of fault tree was improved by introducing new dynamic gates. While the introduction of the dynamic gates helps to overcome many limitations of SFT and allows to analyze a wide range of complex systems, it brings some overhead with it. One such overhead is that the existing combinatorial approaches used for qualitative and quantitative analysis of SFTs are no longer applicable to DFTs. This leads to several successful attempts for developing new approaches for DFT analysis. The methodologies used so far for DFT analysis include, but not limited to, algebraic solution, Markov models, Petri Nets, Bayesian Networks, and Monte Carlo simulation. To illustrate the usefulness of modeling capability of DFTs, many benchmark studies have been performed in different industries. Moreover, software tools are developed to aid in the DFT analysis process. Firstly, in this chapter, we provided a brief description of the DFT methodology. Secondly, this chapter reviews a number of prominent DFT analysis techniques such as Markov chains, Petri Nets, Bayesian networks, algebraic approach; and provides insight into their working mechanism, applicability, strengths, and challenges. These reviewed techniques covered both qualitative and quantitative analysis of DFTs. Thirdly, we discussed the emerging trends in machine learning based approaches to DFT analysis. Fourthly, the research performed for sensitivity analysis in DFTs has been reviewed. Finally, we provided some potential future research directions for DFT-based safety and reliability analysis.
    • Dynamic reliability assessment of flare systems by combining fault tree analysis and Bayesian networks

      Kabir, Sohag; Taleb-Berrouane, M.; Papadopoulos, Y. (2019)
      Flaring is a combustion process commonly used in the oil and gas industry to dispose flammable waste gases. Flare flameout occurs when these gases escape unburnt from the flare tip causing the discharge of flammable and/or toxic vapor clouds. The toxic gases released during this process have the potential to initiate safety hazards and cause serious harm to the ecosystem and human health. Flare flameout could be caused by environmental conditions, equipment failure, and human error. However, to better understand the causes of flare flameout, a rigorous analysis of the behavior of flare systems under failure conditions is required. In this article, we used fault tree analysis (FTA) and the dynamic Bayesian network (DBN) to assess the reliability of flare systems. In this study, we analyzed 40 different combinations of basic events that can cause flare flameout to determine the event with the highest impact on system failure. In the quantitative analysis, we use both constant and time-dependent failure rates of system components. The results show that combining these two approaches allows for robust probabilistic reasoning on flare system reliability, which can help improving the safety and asset integrity of process facilities. The proposed DBN model constitutes a significant step to improve the safety and reliability of flare systems in the oil and gas industry.
    • Dynamic system safety analysis in HiP-HOPS with Petri Nets and Bayesian Networks

      Kabir, Sohag; Walker, M.; Papadopoulos, Y. (2018-06)
      Dynamic systems exhibit time-dependent behaviours and complex functional dependencies amongst their components. Therefore, to capture the full system failure behaviour, it is not enough to simply determine the consequences of different combinations of failure events: it is also necessary to understand the order in which they fail. Pandora temporal fault trees (TFTs) increase the expressive power of fault trees and allow modelling of sequence-dependent failure behaviour of systems. However, like classical fault tree analysis, TFT analysis requires a lot of manual effort, which makes it time consuming and expensive. This in turn makes it less viable for use in modern, iterated system design processes, which requires a quicker turnaround and consistency across evolutions. In this paper, we propose for a model-based analysis of temporal fault trees via HiP-HOPS, which is a state-of-the-art model-based dependability analysis method supported by tools that largely automate analysis and optimisation of systems. The proposal extends HiP-HOPS with Pandora, Petri Nets and Bayesian Networks and results to dynamic dependability analysis that is more readily integrated into modern design processes. The effectiveness is demonstrated via application to an aircraft fuel distribution system.
    • A Framework to Handle Uncertainties of Machine Learning Models in Compliance with ISO 26262

      Vasudevan, Vinod; Abdullatif, Amr R.A.; Kabir, Sohag; Campean, I. Felician (2022)
      Assuring safety and thereby certifying is a key challenge of many kinds of Machine Learning (ML) Models. ML is one of the most widely used technological solutions to automate complex tasks such as autonomous driving, traffic sign recognition, lane keep assist etc. The application of ML is making a significant contributions in the automotive industry, it introduces concerns related to the safety and security of these systems. ML models should be robust and reliable throughout and prove their trustworthiness in all use cases associated with vehicle operation. Proving confidence in the safety and security of ML-based systems and there by giving assurance to regulators, the certification authorities, and other stakeholders is an important task. This paper proposes a framework to handle uncertainties of ML model to improve the safety level and thereby certify the ML Models in the automotive industry.
    • Fuzzy Bayesian estimation and consequence modeling of the domino effects of methanol storage tanks

      Pouyakian, M.; Laal, F.; Jafari, M.J.; Nourai, F.; Kabir, Sohag (2022)
      In this study, a Fuzzy Bayesian network (FBN) approach was proposed to analyze the domino effects of pool fire in storage tanks. Failure probabilities were calculated using triangular fuzzy numbers, the combined Center of area (CoA)/Sum-Product method, and the BN approach. Consequence modeling, probit equations, and Leaky-Noisy OR (L-NOR) gates were used to analyze the domino effects, and modify conditional probability tables (CPTs). Methanol storage tanks were selected to confirm the practical feasibility of the suggested method. Then the domino probability using bow-tie analysis (BTA), and FBN in the first and second levels was compared, and the Ratio of Variation (RoV) was used for sensitivity analysis. The probability of the domino effect in the first and second levels (FBN) was 0.0071472631 and 0.0090630640, respectively. The results confirm that this method is a suitable tool for analyzing the domino effects and using FBN and L-NOR gate is a good way for assessing the reliability of tanks.
    • A fuzzy Bayesian network approach for risk analysis in process industries

      Yazdi, M.; Kabir, Sohag (2017-10)
      Fault tree analysis is a widely used method of risk assessment in process industries. However, the classical fault tree approach has its own limitations such as the inability to deal with uncertain failure data and to consider statistical dependence among the failure events. In this paper, we propose a comprehensive framework for the risk assessment in process industries under the conditions of uncertainty and statistical dependency of events. The proposed approach makes the use of expert knowledge and fuzzy set theory for handling the uncertainty in the failure data and employs the Bayesian network modeling for capturing dependency among the events and for a robust probabilistic reasoning in the conditions of uncertainty. The effectiveness of the approach was demonstrated by performing risk assessment in an ethylene transportation line unit in an ethylene oxide (EO) production plant.
    • Fuzzy evidence theory and Bayesian networks for process systems risk analysis

      Yazdi, M.; Kabir, Sohag (2020)
      Quantitative risk assessment (QRA) approaches systematically evaluate the likelihood, impacts, and risk of adverse events. QRA using fault tree analysis (FTA) is based on the assumptions that failure events have crisp probabilities and they are statistically independent. The crisp probabilities of the events are often absent, which leads to data uncertainty. However, the independence assumption leads to model uncertainty. Experts’ knowledge can be utilized to obtain unknown failure data; however, this process itself is subject to different issues such as imprecision, incompleteness, and lack of consensus. For this reason, to minimize the overall uncertainty in QRA, in addition to addressing the uncertainties in the knowledge, it is equally important to combine the opinions of multiple experts and update prior beliefs based on new evidence. In this article, a novel methodology is proposed for QRA by combining fuzzy set theory and evidence theory with Bayesian networks to describe the uncertainties, aggregate experts’ opinions, and update prior probabilities when new evidences become available. Additionally, sensitivity analysis is performed to identify the most critical events in the FTA. The effectiveness of the proposed approach has been demonstrated via application to a practical system.
    • Fuzzy temporal fault tree analysis of dynamic systems

      Kabir, Sohag; Walker, M.; Papadopoulos, Y.; Rüde, E.; Securius, P. (2016-10)
      Fault tree analysis (FTA) is a powerful technique that is widely used for evaluating system safety and reliability. It can be used to assess the effects of combinations of failures on system behaviour but is unable to capture sequence dependent dynamic behaviour. A number of extensions to fault trees have been proposed to overcome this limitation. Pandora, one such extension, introduces temporal gates and temporal laws to allow dynamic analysis of temporal fault trees (TFTs). It can be easily integrated in model-based design and analysis techniques. The quantitative evaluation of failure probability in Pandora TFTs is performed using exact probabilistic data about component failures. However, exact data can often be difficult to obtain. In this paper, we propose a method that combines expert elicitation and fuzzy set theory with Pandora TFTs to enable dynamic analysis of complex systems with limited or absent exact quantitative data. This gives Pandora the ability to perform quantitative analysis under uncertainty, which increases further its potential utility in the emerging field of model-based design and dependability analysis. The method has been demonstrated by applying it to a fault tolerant fuel distribution system of a ship, and the results are compared with the results obtained by other existing techniques.
    • A hybrid modular approach for dynamic fault tree analysis

      Kabir, Sohag; Aslansefat, K.; Sorokos, I.; Papadopoulos, Y.; Konur, Savas (2020-05)
      Over the years, several approaches have been developed for the quantitative analysis of dynamic fault trees (DFTs). These approaches have strong theoretical and mathematical foundations; however, they appear to suffer from the state-space explosion and high computational requirements, compromising their efficacy. Modularisation techniques have been developed to address these issues by identifying and quantifying static and dynamic modules of the fault tree separately by using binary decision diagrams and Markov models. Although these approaches appear effective in reducing computational effort and avoiding state-space explosion, the reliance of the Markov chain on exponentially distributed data of system components can limit their widespread industrial applications. In this paper, we propose a hybrid modularisation scheme where independent sub-trees of a DFT are identified and quantified in a hierarchical order. A hybrid framework with the combination of algebraic solution, Petri Nets, and Monte Carlo simulation is used to increase the efficiency of the solution. The proposed approach uses the advantages of each existing approach in the right place (independent module). We have experimented the proposed approach on five independent hypothetical and industrial examples in which the experiments show the capabilities of the proposed approach facing repeated basic events and non-exponential failure distributions. The proposed approach could provide an approximate solution to DFTs without unacceptable loss of accuracy. Moreover, the use of modularised or hierarchical Petri nets makes this approach more generally applicable by allowing quantitative evaluation of DFTs with a wide range of failure rate distributions for basic events of the tree.
    • Internet of Things and Safety Assurance of Cooperative Cyber-Physical Systems: Opportunities and Challenges

      Kabir, Sohag (IEEE, 2021-06)
      The rise of artificial intelligence in parallel with the fusion of the physical and digital worlds is sustained by the development and progressive adoption of cyber-physical systems (CPSs) and the Internet of Things (IoT). Cooperative and autonomous CPSs have been shown to have significant economic and societal potential in numerous domains, where human lives and the environment are at stake. To unlock the full potential of such systems, it is necessary to improve stakeholders' confidence in such systems, by providing safety assurances. Due to the open and adaptive nature of such systems, special attention was invested in the runtime assurance, based on the real-time monitoring of the system behaviour. IoT-enabled multi-agent systems have been widely used for different types of monitoring applications. In this paper, we discuss the opportunities for applying IoT-based solutions for the cooperative CPSs safety assurance through an illustrative example. Future research directions have been drawn based on the identification of the current challenges.
    • Machine Learning for Botnet Detection: An Optimized Feature Selection Approach

      Lefoane, Moemedi; Ghafir, Ibrahim; Kabir, Sohag; Awan, Irfan U. (2021-12)
      Technological advancements have been evolving for so long, particularly Internet of Things (IoT) technology that has seen an increase in the number of connected devices surpass non IoT connections. It has unlocked a lot of potential across different organisational settings from healthcare, transportation, smart cities etc. Unfortunately, these advancements also mean that cybercriminals are constantly seeking new ways of exploiting vulnerabilities for malicious and illegal activities. IoT is a technology that presents a golden opportunity for botnet attacks that take advantage of a large number of IoT devices and use them to launch more powerful and sophisticated attacks such as Distributed Denial of Service (DDoS) attacks. This calls for more research geared towards the detection and mitigation of botnet attacks in IoT systems. This paper proposes a feature selection approach that identifies and removes less influential features as part of botnet attack detection method. The feature selection is based on the frequency of occurrence of the value counts in each of the features with respect to total instances. The effectiveness of the proposed approach is tested and evaluated on a standard IoT dataset. The results reveal that the proposed feature selection approach has improved the performance of the botnet attack detection method, in terms of True Positive Rate (TPR) and False Positive Rate (FPR). The proposed methodology provides 100% TPR, 0% FPR and 99.9976% F-score.
    • A method for temporal fault tree analysis using intuitionistic fuzzy set and expert elicitation

      Kabir, Sohag; Goek, T.K.; Kumar, M.; Yazdi, M.; Hossain, F. (2020)
      Temporal fault trees (TFTs), an extension of classical Boolean fault trees, can model time-dependent failure behaviour of dynamic systems. The methodologies used for quantitative analysis of TFTs include algebraic solutions, Petri nets (PN), and Bayesian networks (BN). In these approaches, precise failure data of components are usually used to calculate the probability of the top event of a TFT. However, it can be problematic to obtain these precise data due to the imprecise and incomplete information about the components of a system. In this paper, we propose a framework that combines intuitionistic fuzzy set theory and expert elicitation to enable quantitative analysis of TFTs of dynamic systems with uncertain data. Experts’ opinions are taken into account to compute the failure probability of the basic events of the TFT as intuitionistic fuzzy numbers. Subsequently, for the algebraic approach, the intuitionistic fuzzy operators for the logic gates of TFT are defined to quantify the TFT. On the other hand, for the quantification of TFTs via PN and BN-based approaches, the intuitionistic fuzzy numbers are defuzzified to be used in these approaches. As a result, the framework can be used with all the currently available TFT analysis approaches. The effectiveness of the proposed framework is illustrated via application to a practical system and through a comparison of the results of each approach.
    • Model-based assessment of energy-efficiency, dependability, and cost-effectiveness of waste heat recovery systems onboard ship

      Lampe, J.; Rüde, E.; Papadopoulus, Y.; Kabir, Sohag (2018-06-01)
      Technological systems are not merely designed with a narrow function in mind. Good designs typically aim at reducing operational costs, e.g. through achieving high energy efficiency and improved dependability (i.e. reliability, availability and maintainability). When there is a choice of alternative design options that perform the same function, it makes sense to compare alternatives so that the variant that minimises operational costs can be selected. In this paper, we examine this issue in the context of the design of Waste Heat Recovery Systems (WHRS) for main engines of large commercial freight vessels. We propose a method that can predict the operational cost of a WHRS via thermodynamic analysis which shows costs related to energy utilisation, and dependability analysis which shows costs related to system unavailability and repair. Our approach builds on recent advances in thermodynamic simulation and compositional dependability analysis techniques. It is a model-based approach, and allows reuse of component libraries, and a high degree of automation which simplify application of the method. Our case study shows that alternative designs can be explored in fast iterations of this method, and that this facilitates the evidence-based selection of a design that minimises operational costs.
    • Model-based dependability analysis: State-of-the-art, challenges, and future outlook

      Sharvia, S.; Kabir, Sohag; Walker, M.; Papadopoulos, Y. (2015-01)