Reliability analysis of dynamic systems by translating temporal fault trees into Bayesian networks

Classical combinatorial fault trees can be used to assess combinations of failures but are unable to capture sequences of faults, which are important in complex dynamic systems. A number of proposed techniques extend fault tree analysis for dynamic systems. One of such technique, Pandora, introduces temporal gates to capture the sequencing of events and allows qualitative analysis of temporal fault trees. Pandora can be easily integrated in model-based design and analysis techniques. It is, therefore, useful to explore the possible avenues for quantitative analysis of Pandora temporal fault trees, and we identify Bayesian Networks as a possible framework for such analysis. We describe how Pandora fault trees can be translated to Bayesian Networks for dynamic dependability analysis and demonstrate the process on a simplified fuel system model. The conversion facilitates predictive reliability analysis of Pandora fault trees, but also opens the way for post-hoc diagnostic analysis of failures

A probabilistic model for information and sensor validation

This paper develops a new theory and model for information and sensor validation. The model represents relationships between variables using Bayesian networks and utilizes probabilistic propagation to estimate the expected values of variables. If the estimated value of a variable differs from the actual value, an apparent fault is detected. The fault is only apparent since it may be that the estimated value is itself based on faulty data. The theory extends our understanding of when it is possible to isolate real faults from potential faults and supports the development of an algorithm that is capable of isolating real faults without deferring the problem to the use of expert provided domain-specific rules. To enable practical adoption for real-time processes, an any time version of the algorithm is developed, that, unlike most other algorithms, is capable of returning improving assessments of the validity of the sensors as it accumulates more evidence with time. The developed model is tested by applying it to the validation of temperature sensors during the start-up phase of a gas turbine when conditions are not stable; a problem that is known to be challenging. The paper concludes with a discussion of the practical applicability and scalability of the model

Safety analysis of plugging and abandonment of oil and gas wells in uncertain conditions with limited data

Well plugging and abandonment are necessitated to ensure safe closure of a non-producing offshore asset. Little or no condition monitoring is done after the abandonment operation, and data are often unavailable to analyze the risks of potential leakage. It is therefore essential to capture all inherent and evolving hazards associated with this activity before its implementation. The current probabilistic risk analysis approaches such as fault tree, event tree and bowtie though able to model potential leak scenarios; these approaches have limited capabilities to handle evolving well conditions and data unavailability. Many of the barriers of an abandoned well deteriorates over time and are dependent on external conditions, making it necessary to consider advanced approaches to model potential leakage risk. This paper presents a Bayesian network-based model for well plugging and abandonment. The proposed model able to handle evolving conditions of the barriers, their failure dependence and, also uncertainty in the data. The model uses advanced logic conditions such as Noisy-OR and leaky Noisy-OR to define the condition and data dependency. The proposed model is explained and tested on a case study from the Elgin platform's well plugging and abandonment failure

Fault Tree Analysis: a survey of the state-of-the-art in modeling, analysis and tools

Fault tree analysis (FTA) is a very prominent method to analyze the risks related to safety and economically critical assets, like power plants, airplanes, data centers and web shops. FTA methods comprise of a wide variety of modelling and analysis techniques, supported by a wide range of software tools. This paper surveys over 150 papers on fault tree analysis, providing an in-depth overview of the state-of-the-art in FTA. Concretely, we review standard fault trees, as well as extensions such as dynamic FT, repairable FT, and extended FT. For these models, we review both qualitative analysis methods, like cut sets and common cause failures, and quantitative techniques, including a wide variety of stochastic methods to compute failure probabilities. Numerous examples illustrate the various approaches, and tables present a quick overview of results

DYNAMIC PROBABILITY FAILURE USING BAYESIAN NETWORK FOR HYDROGEN INFRASTRUCTURE MODELING

To produce large scale hydrogen production, it requires adequate and efficient risk control. For decades, fault tree analysis was the most widely used tool for risk assessment for industrial sector generally and hydrogen infrastructure particularly in terms of risk and consequences associated to it. The limitation to this tool is it tends to be static and do not develop over time which can give unreliable estimation of risk. The purpose of this project is to study the suitability and efficiency of dynamic Bayesian Networks in terms of projecting the risk probability failure that develop over time for hydrogen infrastructure as the alternative of the fault tree analysis. In this study, only the risk probability failure is covered without further exploration on the consequences of the risk. The process involved by the conversion of fault tree to Bayesian Networks model by using appropriate framework. Then, the conditional probability table is assigned to each node where the numbers of CPT depend on the numbers of relationship between nodes. Finally the temporal reasoning is done to show the time-invariant between each node and the beliefs is updated to get the results. The ways of inference use for this study are filtering and smoothing. The results show that generally, the OR gates contribute to higher risk probability compare to AND gates. Besides that, the probability for hydrogen activities increase from year to year with the assumption the accident did not happen the previous year. In addition, the instantaneous release incident is relatively low and unlikely to happen compare to the continuous release

Probabilistic Guarantees for Safe Deep Reinforcement Learning

Deep reinforcement learning has been successfully applied to many control tasks, but the application of such agents in safety-critical scenarios has been limited due to safety concerns. Rigorous testing of these controllers is challenging, particularly when they operate in probabilistic environments due to, for example, hardware faults or noisy sensors. We propose MOSAIC, an algorithm for measuring the safety of deep reinforcement learning agents in stochastic settings. Our approach is based on the iterative construction of a formal abstraction of a controller's execution in an environment, and leverages probabilistic model checking of Markov decision processes to produce probabilistic guarantees on safe behaviour over a finite time horizon. It produces bounds on the probability of safe operation of the controller for different initial configurations and identifies regions where correct behaviour can be guaranteed. We implement and evaluate our approach on agents trained for several benchmark control problems

Dynamic safety analysis of decommissioning and abandonment of offshore oil and gas installations

The global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical Bayesian Analysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation.The global oil and gas industry have seen an increase in the number of installations moving towards decommissioning. Offshore decommissioning is a complex, challenging and costly activity, making safety one of the major concerns. The decommissioning operation is, therefore, riskier than capital projects, partly due to the uniqueness of every offshore installation, and mainly because these installations were not designed for removal during their development phases. The extent of associated risks is deep and wide due to limited data and incomplete knowledge of the equipment conditions. For this reason, it is important to capture every uncertainty that can be introduced at the operational level, or existing hazards due to the hostile environment, technical difficulties, and the timing of the decommissioning operations. Conventional accident modelling techniques cannot capture the complex interactions among contributing elements. To assess the safety risks, a dynamic safety analysis of the accident is, thus, necessary. In this thesis, a dynamic integrated safety analysis model is proposed and developed to capture both planned and evolving risks during the various stages of decommissioning. First, the failure data are obtained from source-to-source and are processed utilizing Hierarchical Bayesian Analysis. Then, the system failure and potential accident scenarios are built on bowtie model which is mapped into a Bayesian network with advanced relaxation techniques. The Dynamic Integrated Safety Analysis (DISA) allows for the combination of reliability tools to identify safetycritical causals and their evolution into single undesirable failure through the utilisation of source to-source variability, time-dependent prediction, diagnostic, and economic risk assessment to support effective recommendations and decisions-making. The DISA framework is applied to the Elgin platform well abandonment and Brent Alpha jacket structure decommissioning and the results are validated through sensitivity analysis. Through a dynamic-diagnostic and multi-factor regression analysis, the loss values of accident contributory factors are also presented. The study shows that integrating Hierarchical Bayesian Analysis (HBA) and dynamic Bayesian networks (DBN) application to modelling time-variant risks are essential to achieve a well-informed decommissioning decision through the identification of safety critical barriers that could be mitigated against to drive down the cost of remediation