From Probabilistic Counterexamples via Causality to Fault Trees

In order to take advantage of the computer in the numerical calculation, in view of the fault tree qualitative analysis, a new method of fault tree analysis, numerical methods, was proposed on the basis of absorbing merits of the prime number method and determinant method. The fault tree's minimum cut sets and path sets can be calculated effectively with this method. Consequently, a specific example about calculation of the minimum cut sets was demonstrated with Scilab in this paper.

Summary Such classic risk-assessment techniques as failure mode and effect analysis(FMEA) and fault-tree analysis (FTA) are in common use by engineers but have anumber of shortcomings. Dynamic simulation, a new computer technique, countersmany of these deficiencies by introducing a time element that accommodatesrealistic component-failure characteristics and testing and inspectionactivities. This paper demonstrates how FMEA and FTA have been used to helpdesign North Sea well completions and how dynamic simulation models complexsystems realistically to allow comparison of designs and to provide insightinto operating availability and component-testing frequencies. Introduction Both single- and dual-string gas-lift completions are commonly used in theNorth Sea, but it is not obvious which design provides greater safely andreliability. The single completion (Fig. 1) with annular safety valve issimpler to operate and to work over, but during normal production, it has alarge volume of high-pressure gas beneath the tubing hanger that acts on thehanger, tubing spool, production casing, and wellhead seals. The alternativedual-string design (Fig. 2), in which production is routed through the longstring and injection gas through the second or short string, reduces the gasinventory above the safety valve and removes the lift-gas pressure from theannulus. It is a more complicated system to operate and to maintain, however, and it has more component parts. To assist with the selection of completiontype, a conventional risk analysis was undertaken to rank the risks associatedwith each completion during both production and workover modes and to identifycritical components to allow for possible design changes. An FMEA provided aqualitative comparison of the two designs, and an FTA quantified the risks. While these static techniques successfully identified the critical components, they could not address such problems as changes in the well system withproblems as changes in the well system with time, undetected failures, effectsof inspection and maintenance, or components with failure distributions otherthan exponential. Use of a dynamic simulator, which simulates the performanceof the completion throughout many life cycles, provided a more realistic riskassessment and allowed sensitivity tests to be performed on maintenance andtest frequencies and undetected failures. We assumed that such conditions aswater cut and reservoir pressure were identical for both completion types. Theoretically, the influence of such conditions and the effect of varyingequipment sizes can be modeled by adjusting the reliability data, but eachspecific situation requires its own analysis. Reliability data were provided byRefs. 1 and 2, in addition to some proprietary data. Uncertainties are alwaysassociated with reliability data, and the application of the data isnecessarily based on judgment. Risks expressed numerically should be treated ascomparative rather than absolute. Note that, in the static and dynamic studiespresented here, different types of failure presented here, different types offailure distributions were used because more realistic distributions dmexponential ones were available for use in the dynamic simulation. Static Analysis FMEA. After definition of the two well systems to be compared (Figs. 1 and2), the first step in assessing the reliability of each system was to preparean FMEA. This involved identification of possible component failures and theirconsequences to provide a qualitative comparison of the inherent risks in eachsystem. To provide a basis for comparison of the FMEA for each well system, weprepared a list of very critical events (VCE's) prepared a list of verycritical events (VCE's) and critical events (CE's) to summarize each analysis. Events were defined as follows. VCE (Severity 1): loss of control of the well, possible loss of life, and evacuation necessary. CE (Severity 2): loss of oneor more safety barriers but not all barriers, evacuation possible, and majorworkover required. possible, and major workover required. Comparison of VCE'sand CE's for each system provided a qualitative assessment as to which systemwas likely to be more or less hazardous. FTA. FTA techniques previously applied to well-completion designs were usedin this study to provide a quantitative comparison of different designs. TheFTA is based on the component identification carried out in the FMEA, but theFTA differs from the FMEA because it is a top-down approach. Therefore, the topevent of interest (i.e., catastrophic wellhead-system failure) is defined and alogic tree is constructed of lower-level failures until the component-failurelevel is reached at the bottom of the tree. JPT P. 1374

Sand casting operations, though commonplace, pose a significant threat of explosion accidents. This paper presents a novel sand casting safety assessment technique based on fault tree analysis, Heinrich accident triangle, hazard and operability–layer of protection analysis (HAZOP–LOPA) and bow tie model components. Minimal cut sets and minimal path sets are first determined based on fault tree analysis, then the frequency of sand casting explosion accidents is calculated based on the Heinrich accident triangle. Third, the risk level of venting quality can be reduced by adopting HAZOP–LOPA; the residual risk level of venting quality remains excessive even after adopting two independent protective layers. The bow tie model is then adopted to determine the causes and consequences of venting quality. Five preventative measures are imposed to enhance the venting quality of foundry sand accompanied by 16 mitigative safety measures. Our results indicate that the risk attributable to low foundry sand venting quality can be minimized via bow tie analysis.

Component failures can result in catastrophic behaviors in safety critical embedded systems, sometimes resulting in loss of life. Component failures can be treated as off nominal behaviors (ONBs) with respect to the components and sub systems involved in an embedded system. A lot of research is being carried out to tackle the problem of ONBs. These approaches are mainly focused on the states (i.e., desired and undesired states of a system at a given point of time to detect ONBs). In this paper, an approach is discussed to detect component failures and critical components of an embedded system. The approach is based on fault tree analysis (FTA), applied to the requirements specification of embedded systems at design time to find out the relationship between individual component failures and overall system failure. FTA helps in determining both qualitative and quantitative relationship between component failures and system failure. Analyzing the system at design time helps in detecting component failures and critical components and helps in devising strategies to mitigate component failures at design time and improve overall safety and reliability of a system.

Summary & ConclusionsWhen designing a digital instrumentation and control (DI&C) system, flaws in the architecture or software, failures of components, cyber-attacks, and human failures can lead to unintended effects on the system under control. There are several approaches for assessing potential hazards from inappropriate operation of a system, but that the best approach is likely a combination of assessment techniques [1]. The approach that showed the most potential was an integration of Systems Theoretic Process Analysis (STPA) with Fault Tree Analysis (FTA).While the STPA process is very effective at identifying things that could go wrong with the system, it does not have any tools to prioritize the Unsafe Control Actions (UCAs) to identify which UCAs are the most important. When a fault tree is available for assessing the risk of a system, the integration of FTA with STPA can be used to assign a risk rank to the UCAs. This allows the system designer to prioritize either design changes or control methods to address each UCA appropriately. This paper demonstrates one approach for integrating FTA and STPA for risk ranking the UCAs identified by the STPA.This integrated process allows the designer to focus on the most important aspects of the design when transitioning from conceptual design to preliminary design to detailed design. At each step, the design is informed by the STPA and FTA to make the final design safer and more reliable. The result is a final design that is complete, accurately specified, and has a low risk of emergent behaviors.

Uncertainty in fault tree analysis: A fuzzy approach

Computer-aided fault tree synthesis II. Fault tree construction

Computer-aided fault tree synthesis III: Real-time fault location

We present SymLearn, a method to automatically infer fault tree (FT) models from data. SymLearn takes as input failure data of the system components and exploits evolutionary algorithms to learn a compact FT matching the input data. SymLearn achieves scalability by leveraging two common phenomena in FTs: (i) We automatically identify symmetries in the failure data set, learning symmetric FT parts only once. (ii) We partition the input data into independent modules, subdividing the inference problem into smaller parts.We validate our approach via case studies, including several truss systems, which are symmetric structures commonly found in infrastructures, such as bridges. Our experiments show that, in most cases, the exploitation of modules and symmetries accelerates the FT inference from hours to under three minutes.

Fault tree analysis of a system can provide a complete characterization of system failure modes, i.e., what combinations of component failures can give rise to system failure. This can be applied to the design process at several levels: (1) confirmatory analysis, in which a fault tree development is used to verify design adequacy, (2) importance analysis, in which fault tree analysis is used to highlight system vulnerabilities, and (3) design optimization, in which fault tree analysis is used to pick the least expensive configuration from a collection of possibilities satisfying a given constraint. Experience shows that the complexity of real systems warrants the systematic and structured development of fault trees for systems whose failure can have severe consequences.

ABSTRACTThis study aimed to use the fault tree analysis (FTA) to investigate the casual relationship among the event and causes contributing to extremely severe crash that leads to more than 10 deaths. The characteristics of extremely severe traffic crashes were compared with those of normal crashes in terms of gender, driving experience, road technical level, road alignment, time, and weather conditions. A fault tree was developed to provide an overview of the basic causes and their causal relationship. The minimal cut sets were derived to reveal the causal relationships among events and causes contributing to extremely severe crashes. Then, quantitative assessment was conducted to evaluate the importance of various events. The results can help to prioritize actions and resources for crash prevention. Finally, the minimal path set analysis was conducted to identify the minimal sets of basic events needed to be prevented so that extremely severe crash will not occur.

While offshore salt caverns, as part of the carbon capture and storage (CCS) systems, could offer significant advantages in storage carbon dioxide, they are still not widely implemented, partially due the challenges in maintaining the safe operation of such systems with a long-projected storage time. The presented study provided a Fault Tree (FT) analysis for the causal relationships of storage system failures with respect to the root causes, and further the potential integration of the FT with the dynamic Bayesian network (DBN) in order to monitor offshore salt caverns in the long-term operations. The presented study can incorporate both physics-of-failure models and sensory data to update the failure probabilities of the components in the offshore salt cavern storage system. The fault tree considers unintentional flow as the main failure mode and divides the system into three subsystems: the wellhead, the well and the cavern itself. Then, each individual subsystem is studied based on the components in the subsystem and the NORSOK standards for safe well design. The fault tree presented in this study also outlines the relationship between the failure modes and the effect they have on the system failure. Th fault tree analysis tool can then be converted into a DBN to provide a long-term monitoring framework for offshore CCS systems with limited observability, where real-time data can be utilized in order to update the probabilities of failure of components over time.

In modern conditions of intensive operation of air transport, ensuring the reliability and failure-free operation of technical systems is a priority task, directly related to flight safety and economic efficiency [1]. Statistical data analysis indicates that technical malfunctions, ranking second among the causes of flight cancellations, create significant operational and financial risks. The problem is particularly acute in the context of aircraft engines, which are critically important components whose failure can have catastrophic consequences [1]. A crucial factor is also the time deficit between flights necessary for scheduled maintenance, which requires the implementation of reliable predictive diagnostic methods. Traditional approaches to reliability assessment, based on classic analytical models (e.g., the exponential distribution law), although fundamental, often prove insufficiently sensitive to reflect the multifactorial and non-linear nature of wear and failure processes in complex modern systems [2]. In this regard, there is an urgent need to develop integrated methodologies capable of combining the structural logic of engineering analysis with the predictive power of artificial intelligence (AI). The article proposes an integration that combines the classical Fault Tree Analysis (FTA) methodology with machine learning algorithms, namely Long Short-Term Memory (LSTM) recurrent neural networks [3]. The FTA methodology provides the necessary structural framework, establishing cause-and-effect relationships between basic events (component failures) and the top event (engine failure) through logical operators. The key idea is to use LSTM networks for the dynamic prediction of the probability of failure Pi (t+Δt) of each individual critical system node. LSTM networks, known for their ability to effectively process large volumes of sequential data (time series), are trained on historical engine operational parameter data, which includes temperature, pressure, vibration, and other critical indicators Xi (t) [4]. This allows the model to capture complex, long-term dependencies that precede the actual failure. The predicted failure probabilities Pi (t+Δt) of individual components are dynamically substituted into the FTA logical model (equation P = f (P1, P2, ..., Pn)), which allows for the real-time updating of the integrated reliability assessment of the entire system Psys(t). This mechanism not only increases the accuracy of the overall prediction but also allows for the operational reduction of the fault tree, narrowing the scope of the search for the most probable scenarios. As a result, engineering and technical personnel can quickly identify the source of a potential malfunction, which is critically important for transitioning to condition-based maintenance and minimizing the risks of unexpected failures.

Dynamic reliability analysis framework using fault tree and dynamic Bayesian network: A case study of NPP

A sterilizer is a pressurized steam vessel used to boil palm oil. The condition of the sterilizer at PT .X often emits steam at the door and body of the stew. Throughout 2020, there were 12 critical components that were frequently damaged, such as ball valve, actuator, exhaust valve, packing door, elbow, condensate nozzle, liner, pipe, condensate valve, strainer valve, pipe flange, and packing flange. Fault Tree Analysis is an analysis tool that graphically translates the combinations of errors that cause system failures. Reliability Block Diagram is a diagramming method for showing how reliability components contribute to the success or failure of a complex system. Based on the results of the failure calculation using fault tree analysis, the probability of failure of the horizontal sterilizer component is the ball valve 12.2%, exhaust valve 10.9% actuator 6%, door packing 0.24%, elbow 0.24%, condensate nozzle 4.8%, liner 8.61%, 0.25% pipe, 0.21% condensate valve, 4.4% filter valve, 0.22% pipe flange and 0.27% packing flange. The reliability value of the horizontal sterilizer from the calculation using the reliability block diagram is 85.69% if it operates for 8 hours, 62.93% if it operates for 27 hours, 39.6% if it operates for 54 hours, 13.34% if it operates for 117 hours. o'clock. o'clock. o'clock. hours and 1.81% when operating for 234 hours. To maintain reliability above 60%, the preventive maintenance schedule is: Every 80 hours of operation a door packing inspection is carried out. Every 234 hours of operation, elbow tubing and flanges are checked. Every 300 hours of operation, a pipe inspection is carried out. Every 450 operational hours an inspection is carried out on the ball valve, condensate nozzle, liner, actuator, and exhaust valve. Every 30 hours of operation, valve condensate, filter valves and packing flanges are checked.