Safety analysis of complex systems based on Bayesian Networks

Event trees are a popular technique for modelling accidents in system safety analyses. Bayesian networks are a probabilistic modelling technique representing influences between uncertain variables. Although popular in expert systems, Bayesian networks are not used widely for safety. Using a train derailment case study, we show how an event tree can be viewed as a Bayesian network, making it clearer when one event affects a later one. Since this effect needs to be understood to construct an event tree correctly, we argue that the two notations should be used together. We then show how the Bayesian Network enables the factors that influence the outcome of events to be represented explicitly. In the case study, this allowed the train derailment model to be generalised and applied in more circumstances. Although the resulting model is no longer just an event tree, the familiar event tree notation remains useful.

A novel approach for software reliability analysis of digital instrumentation and control systems in nuclear power plants

Safety analysis is a key activity for the development of railway transportation systems. The analysis is used not only to determine whether new systems do not expose the general public to unduly high levels of risks, but also to inform the development process itself through identification and allocation of appropriate requirements. Whilst inadequate and inaccurate safety analysis may have potentially catastrophic consequences, it is rarely validated in practice. This paper presents a case for validation of safety analysis of the railway systems along with a technical approach to such validation. The approach is based on the concept of Failure Injection and the Lightweight Refinement relation. The paper further argues that the safety analysis validation should be embedded in a wider uncertainty management framework and shows how this can be made consistent with the spirit of the existing safety standards of the railway domain.

Automatic safety analysis of control systems

In the first part of the article is a methodology for implementing a model-driven safety analysis of dynamical technology systems. The safety analysis is performed in the development of control system, mainly aiming at critical processes of system operation. I divided my methodology into five steps. These steps of the methodology was carried out in a hierarchical sequence. The objective of the second part of the article is to demonstrate the principle of the SQMD method concept for performing safety analysis on the example of a dynamical system. By SQMD method will be developed a models of safety-critical processes by which they will be carried out on-line monitoring. The method combining qualitative and quantitative procedures of modelling and taking both methods advantages.

Mechatronic systems become ever more complex because of their increasing number of interconnected safety critical components and sophistication. MBSE (Model‐based Systems Engineering) and MBSA (Model‐Based Safety Assessment) are the most commonly adopted approaches to deal with the design and safety analysis of mechatronic systems. Unfortunately, both approaches are normally adopted separately, especially in the earlier phases of system design, thus leading to a lack of communication between system engineers and the safety team. This work aims to fill that gap at a high level, that is, through process interaction. This paper proposes an enhanced V‐model for the design of safety‐critical mechatronic systems. It relates a system development process with specific safety assessment methods. Specifically, the proposed workflow details exchange flows between the RFLP (Requirements, Functional, Logical, Physical) method, the FHA (Functional Hazard Analysis), the FMEA (Failure Mode and Effects Analysis), the MBSA and simulation, and the FTA (Fault Tree Analysis). These analyses are complemented with multiphysics modeling and simulation to observe system behavior in functional and failure scenarios, with the aim of requirements verification. The design workflow has been applied to a winged Unmanned Aerial Vehicle to apply the parallel process and the necessary interaction of MBSE and MBSA approaches. The information flows between the individual activities proved effective for designing a safe system before the verification phase. The main benefit of the proposed workflow is providing both the design and safety team with some interaction points, thus avoiding a lack of safety‐critical analysis in the early phases of system design.

Dynamic safety analysis of process systems using nonlinear and non-sequential accident model

This paper presents an approach for reliability analysis of repairable systems with two-unit parallel structure considering Common Cause Failure (CCF) and maintenance correlation based on GO methodology. First, the GO algorithm for dealing with CCF is introduced. Then, the common cause failure probability formulas of two-unit parallel structure considering maintenance correlation are deduced based on Markov theory. Furthermore, the analysis process of the new GO method is formulated. Finally, the dynamic availability analysis of HTOSS is conducted by the GO method. And the result is compared with the result of system considering CCF, and the result of system without considering CCF and maintenance correlation. The results show that the CCF and maintenance correlation are not ignored for reliability analysis of such system. All in all, this study not only widens the application of GO method. But it also provides guidance and an approach for reliability analysis of repairable systems with two-unit parallel structure considering CCF and maintenance correlation.

Cyber-Physical Systems (CPSs) represent a systems category developed and promoted in the maritime industry to automate functions and system operations. In this study, a novel Combinatorial Approach for Safety Analysis is presented, which addresses the traditional safety methods’ limitations by integrating System Theoretic Process Analysis (STPA), Events Sequence Identification (ETI) and Fault Tree Analysis (FTA). The developed method results in the development of a detailed Fault Tree that captures the effects of both the physical components/subsystems and the software functions’ failures. The quantitative step of the method employs the components’ failure rates to calculate the top event failure rate along with importance metrics for identifying the most critical components/functions. This method is implemented for an exhaust gas open loop scrubber system safety analysis to estimate its failure rate and identify critical failures considering the baseline system configuration as well as various alternatives with advanced functions for monitoring and diagnostics. The results demonstrate that configurations with SOx sensor continuous monitoring or scrubber unit failure diagnosis/prognosis lead to significantly lower failure rate. Based on the analysis results, the advantages/disadvantages of the novel method are also discussed. This study also provides insights for better safety analysis of the CPSs.

Safety analysis of mechatronic systems is a time-consuming activity, because of the complexity of these systems that involve different fields of engineering. It is desirable to carry out safety assessment methods as soon as possible in the design process in order to reduce errors, cost and time to market of the system. Our paper addresses this problem by proposing a safety profile that is integrated directly with the modeling elements of the system via SysML, a model-based systems engineering language. Failure modes of each function and each component, their causes, their effects as well as their severity are modeled via stereotypes or tag definitions that extend the existing UML elements. These failure data can be 1) entered directly by systems engineers when possible; and then 2) generated automatically for safety experts' work; and also 3) updated from safety analysis results. Our integrated systems engineering and safety analysis process helps to narrow the gap between these two disciplines by ensuring the consistency in the whole process. A case study with an electromechanical actuator is given to illustrate the process as well as the safety profile.

Elimination of mother-to child transmission (eMTCT) of HIV is a complex cascade of interventions longitudinally administered during pregnancy, delivery and after, until the baby is no longer at risk of infection. Systems Analysis and Improvement Approach (SAIA) an institutional implementation strategy that packages systems engineering tools have in clinical trial setting been shown to effectively optimize performance. This study evaluated feasibility of institutionalizing inter-professional mentoring teams (IP-MT) that utilize SAIA for strengthening delivery of eMTCT services at referral and teaching hospitals affiliated to the Faculties of Health sciences. Nurse midwives drawn from 15 referral and teaching hospitals affiliated to faculties of Health Sciences of 4 Universities in Kenya and who were trained on SAIA were tasked to mobilize fellow colleagues to build IP-MT to support eMTCT within their hospital following additional 2-day online SAIA training and follow-up mentorship as needed. In the current training 71 health workers of different cadres were trained. Retention of trained personnel was 37.5% and 92.9% for the first and second cohort respectively. Nine facilities established the IP-MT but only one hospital had participation of a consultant doctor. Eight hospitals were able to analyze their facility performance for the period preceding the training using the PMCT cascade analysis tool (P-CAT). Two-thirds of expected pregnant women booked into care. Four of the eight hospitals had consistent fidelity across all components of the eMTCT service. Two hospitals failed to provide anti-retroviral drugs to 50% of eligible women. Timely early infant diagnosis occurred in 271 (92.5%) of the 293 identified HIV exposed infants. Teaching and referral hospitals affiliated to University health professions schools offer opportunity for interprofessional collaboration and opportunity to improve the quality of eMTCT services. Collation of the P-CAT findings helped identify common program gaps and facility specific deficiency in delivery of eMTCT.

The prediction of change propagation is one of the important issues in engineering change management. The aim of this article is to explore the capability of Bayesian network (BN), which is an emerging tool for a wide range of risk management, in modeling and analysis of change propagation. To this end, we compare the BN with change prediction method (CPM), which is the most established probabilistic methods for predicting change propagation. This paper shows that a CPM-based model can be converted into an equivalent BN, and the probabilistic inference technique on the latter results in the same change prediction result obtained from the former. Then, this paper shows that several improvements can be obtained at various levels using the BN. At the modeling level, complex relationship between components such as combined effect of simultaneous changes or multistate relationship can be naturally represented with the BN. At the analysis level, various change propagation scenarios can be analyzed using probabilistic inference on the BN. Finally, BN provides a robust framework for learning change propagation probabilities from empirical data. The case study is conducted to show the feasibility of the model.

Safety analysis is very important to prevent events that may cause catastrophic accidents in process industries. Although traditional safety assessment methods including fault tree (FT) have been used widely in this field, they still lack in dynamic structure representation and uncertainty processing, which are important for the process safety analysis. In this study, a safety analysis approach based on the probabilistic Petri-net (PPN), which extends the basic Petri-net, is proposed. Probabilities are assigned to the tokens in the Petri-net, and the probability dependencies of events can then be modeled by a PPN. The enabling rule of a transition and the execution rule of an enabled transition are provided, through which the dynamic evolution process of the system can be revealed by the flowing of tokens, and the probabilities corresponding to the occurrence of events can be updated. A case that vapor cloud may overflow from a tank is studied to illustrate the proposed approach. The result is validated by comparing with that of the fault tree analysis.

In recent work on the safety analysis of systems we have shown how causal relationships amongst events can be algorithmically inferred from probabilistic counterexamples and subsequently be mapped to fault trees. The resulting fault trees were significantly smaller and hence easier to understand than the corresponding probabilistic counterexample, but still contain all information needed to discern the causes for the occurrence of a hazard. More recently we have developed an approach called Causality Checking which is integrated into the state-space exploration algorithms used for qualitative model checking and which is capable of computing causality relationships on-the-fly. The causality checking approach outperforms the probabilistic causality computation in terms of run-time and memory consumption, but can not provide a probabilistic measure. In this paper we combine the strengths of both approaches and propose an approach where the causal events are computed using causality checking and the probability computation can be limited to the causal events. We demonstrate the increase in performance of our approach using several case studies.

The methodology of a multistate approach to complex technical system safety analysis is introduced, and a system safety function is defined. A semi-Markov process for system operation process modelling is used. Furthermore, linking of the multistate system safety model and the system operation process model is carried out to build a general joint safety model of a system operating at variable conditions. This joint model is applied in safety evaluation of the technical system of a ferry operating at sea. The ferry technical system safety is analysed in varying operational conditions, with its safety structure and component safety functions changing in its operational time.