Advanced Fault Tree Analysis for Improved Quality and Risk Assessment

Fault Tree (FT) analysis is not only the most common technique used in engineering practice for the estimation of system reliability, but is also a key tool shared between designers, analysts, and regulators for the safe operation and licensing purposes. In spite of its long lasting success, traditional FT analysis presents significant limitations in modeling a wide range of features frequently encountered in modern systems. The most critical of these is the assumption of failure events independence, which is often not justified by the realistic behavior of the engineering system, undermining modeling accuracy. This article introduces a novel methodology for the analysis of FTs allowing for component dependencies and dynamic features. The proposed approach, based on the use of binary decision diagrams, is demonstrated using a simple numerical application for verification. Its applicability and computational feasibility is discussed in details.

Wind turbines are complex and expensive equipment, requiring high reliability and low maintenance costs. However, most of the existing fault tree analysis (FTA) methods for reliability analysis of wind turbines assume that the repair of wind turbines can restore them to as good as new condition, which is called perfect repair. This assumption may not be realistic in practice, as the repair may not fully recover the original performance or functionality of the equipment or may introduce new defects or errors. This phenomenon is called imperfect repair, which can reduce the reliability of wind turbines over time. To consider the imperfect repair effect in reliability analysis, we present a new FTA approach in this study. In order to predict and assess the failure intensity and dependability of wind turbines under imperfect repair, the proposed FTA technique uses a log-linear proportional intensity model (LPIM). Failure probability, failure rate, and mean time to failure can all be improved with the suggested FTA technique for wind turbines operating with poor repair. The proposed FTA method can also identify the critical components or failure modes most affected by the imperfect repair effect and suggest preventive maintenance actions to improve the reliability of wind turbines. We demonstrate the applicability and effectiveness of the proposed FTA method through a case study on a real or hypothetical wind turbine system under imperfect repair. The findings indicate that the proposed FTA method offers a more precise and authentic assessment of the reliability of wind turbines in the presence of imperfect repair, in contrast to existing FTA methods that assume perfect repair. The findings also demonstrate that the electrical system, hydraulic system, gearbox, generator, and blade are the most critical components or failure modes affecting the system's reliability.

This research analyzes waste material in construction projects using Lean Construction and Fault Tree Analysis approaches. The study was conducted on three building projects: Building X in Makassar, Building Y in Batam, and Building Z in Kediri. The objectives are to estimate waste cost from material waste during structural and architectural work, identify the highest waste material indicators, and determine optimization strategies to improve waste cost efficiency. The Waste Assessment Model method was used to identify waste variables, while Fault Tree Analysis was employed to determine root causes and calculate the probability of waste occurrence. Data collection was conducted through direct field observations, interviews with contractors, and questionnaires. Results showed that waste costs in Building X project amounted to Rp. 1,371,690,630 (4.93% of contract value), Building Y project Rp. 1,434,910,750 (3.75% of contract value), and Building Z project Rp. 1,573,029,639 (3.36% of contract value). Waste Assessment Model analysis revealed three critical waste types across all projects: Defects (19.95-20.09%), Overproduction (18.19-18.32%), and Inventory (16.16-16.27%). Fault Tree Analysis identified that the highest probability variable causing waste material was A1 "Human or Group Failure" (0.6066-0.6565), while the lowest was C2 "Storage Capacity and Method" (0.3040-0.3623). This research provides strategies for waste cost efficiency through identification of dominant causes and probability calculations of waste occurrence.

An innovative logic tree, Failure Expansion Tree (FET), is proposed in this paper, which improves on traditional Fault Tree Analysis (FTA). It describes a different thinking approach for risk factor identification and reliability risk assessment. By providing a more comprehensive and objective methodology, the rather subjective nature of FTA node discovery is significantly reduced and the resulting mathematical calculations for quantitative analysis are greatly simplified. Applied to the Useful Life phase of a subsea pipeline engineering project, the approach provides a more structured analysis by constructing a tree following the laws of physics and geometry. Resulting improvements are summarized in comparison table form.

According to the risk assessment conducted on buried gas pipelines,it is found that the number of factors affecting pipeline failure is more than 130 and that it could not reach the requirement by using a model to calculate or evaluate.On the base of the traditional buried gas pipeline fault tree analysis, this paper puts forward a thought of the grading modeling risk assessment,adopting the compensation fuzzy neural network theory . The fault trees minimal cut sets grading modeling helps to establish the mathematical model of compensation fuzzy neural network risk assessment, deduce the model error transformation formula, revise the assessment errors ,and solve the problem of risk assessment of the large buried gas pipelineswhich needs to consider many assessment factors. The practical study demonstrates that the assessment results are objective and the assessment errors are small.

To obtain refined safety requirements during system architecture design stage, the traditional static fault tree analysis mothed is widely used to analyse the logical relationship between basic hazardous events leading to system hazards in railway signal system, and to identify the weak and key equipment of the system. In order to accurately characterize the dynamic behaviours of the system and improve the accuracy and credibility of analysis results, a dynamic fault tree analysis mothed of railway signal system based on failure propagation modelling was proposed. According to failure logic modelling of dynamic failure behaviours, conforming to signal system function design, component failure model was established within SimFIA platform, and fault trees of system hazards were attained by model simulation. The result of Communication-Based Train Control (CBTC) System on-board equipment case study shows that the fault tree of complex system function are generated by the dynamic fault tree analysis mothed based on Failure Propagation and Transformation Notation (FPTN) modelling, which guaranteed the safety analysis outcomes was accurate and credible.

Traditional fault tree analysis is an effective method to analyze system reliability that is based on static logic, and the system which considering the influence in combination of fuzzy uncertainty and dynamic failure characters is still difficult to estimate precisely. The fuzzy number theor y can fully consider the fuzzy uncertainty of system which use various aspects information, and Markov process can describe the dynamic failure characteristics of the system through the change of state space, then the fuzzy number theory and Markov process is introduced into dynamic fault tree analysis to overcome the disadvantage of traditional fault tree. The method was applied to dynamic fault tree research of an internal combustion engine to prove the validity.

There will be circumstances where partial or conditionally automated vehicles fail to drive safely and require human interventions. Within the human factors community, the taxonomies surrounding control transitions have primarily focused on characterizing the stages and sequences of the transition between the automated driving system (ADS) and the human driver. Recognizing the variance in operational design domains (ODDs) across vehicles equipped with ADS and how variable the takeover situations may be, we describe a simple taxonomy of takeover situations to aid the identification and discussions of takeover scenarios in future takeover studies. By considering the ODD structure and the human information processing stages, we constructed a fault tree analysis (FTA) aimed to identify potential failure sources that would prevent successful control transitions. The FTA was applied in analyzing two real-world accidents involving ADS failures, illustrating how this approach can help identify areas for improvements in the system, interface, or training design to support drivers in level 2 and level 3 automated driving.

Based on the structure and operation mechanism of the dual-stage lithographic system, fault tree analysis for the key subsystem, wafer stage, is performed. Since the system is very complexity, the BDD (binary decision diagram) and DFTA (dynamic fault tree analysis) technique are introduced to make up the disadvantages of the traditional static FTA (fault tree analysis) method. Event “The kinematic accuracy of wafer stage can't meet the goal” is installed as the top event of the fault tree. Central factors affecting the kinematic accuracy of the wafer stage is identified through the study.

PurposeRisk analysis is a critical investigation field for many sectors and organizations to maintain the information management reliable. Since mining is one of the riskiest sectors for both workers and management, comprehensive risk analysis should be carried out. The purpose of this paper is to explore comprehensively the undesired events that may occur during a particular process with their main reasons and to perform a risk analysis for these events, by developing a risk analysis methodology. For performing risk analysis, discovering and defining the potential accidents and incidents including their root causes are important contributions of the study as distinct from the related literature. The fuzzy approach is used substantially to obtain the important inferences about the hazardous process by identifying the critical risk points in the processes. In the scope of the study, the proposed methodology is applied to an underground chrome mine and obtaining significant findings of mining risky operations is targeted.Design/methodology/approachFault tree analysis and fuzzy approach are used for performing the risk analysis. When determining the probability and the consequences of the events which are essential components for the risk analysis, expressions of the heterogeneous expert group are considered by means of the linguistic terms. Fault tree analysis and fuzzy approach present a quiet convenience solution together to specify the possible accidents and incidents in the particular process and determine the values for the basis risk components.FindingsThis study primarily presents a methodology for a comprehensive risk analysis. By implementing the proposed methodology to the underground loading and conveying processes of a chrome mine, 28 different undesired events that may occur during the processes are specified. By performing risk analysis for these events, it is established that the employee’s physical constraint while working with the shovel in the fore area, the falling of materials on employees from the chute and the scaling bar injuries are the riskiest undesired events in the underground loading and conveying process of the mine.Practical implicationsThe proposed methodology provides a confidential and comprehensive method for risk analysis of the undesired events in a particular process. The capability of fault tree analysis for specifying the undesired events systematically and the applicability of fuzzy approach for converting the experts’ linguistic expressions to the mathematical values provide a significant advantage and convenience for the risk analysis.Originality/valueThe major contribution of this paper is to develop a methodology for the risk analysis of a variety of mining accidents and incidents. The proposed methodology can be applied to many production processes to investigate the dangerous operations comprehensively and find out the efficient management strategies. Before performing the risk analysis, determining the all possible accidents and incidents in the particular process using the fault tree analysis provides the effectiveness and the originality of the study. Also, using the fuzzy logic to find out the consequences of the events with experts’ linguistic expressions provides an efficient method for performing risk analysis.

International Maritime Organization (IMO) implements the principles of risk management through a systematic process called Formal Safety Assessment (FSA). FSA is a structured and systematic methodology, aimed at enhancing maritime safety, including protection of life, health, the marine environment and property, by using risk analysis and cost benefit assessment. One of the most widely used hazard identification and risk analysis techniques in FSA is Fault Tree Analysis (FTA) method. However, only basic conventional methodology of the FTA technique is used in FSA. When dealing with large fault trees, the limitations of FTA in terms of accuracy and the efficiency become apparent. It is necessary for IMO to improve FTA method in FSA. In recent years, the Binary Decision Diagram (BDD) method has been developed that solves fault trees and overcomes the disadvantages of the conventional FTA approach. In this paper, the BDD method is introduced for the application in FSA, as it has never been used in FSA studies. A commonly used construction method and some basic event ordering methods of BDD are described. By applying BDD to FTA, two main issues concerned in FSA, minimal cut set and top event probability, can be obtained efficiently and accurately comparing to the analysis using a conventional FTA method.

This paper integrates Fault Tree Analysis (FTA) and Bayesian Networks (BN) to assess collision risk and establish Automotive Safety Integrity Level (ASIL) B failure rate targets for critical autonomous vehicle (AV) components. The FTA-BN integration combines the systematic decomposition of failure events provided by FTA with the probabilistic reasoning capabilities of BN, which allow for dynamic updates in failure probabilities, enhancing the adaptability of risk assessment. A fault tree is constructed based on AV subsystem architecture, with collision as the top event, and failure rates are assigned while ensuring the total remains within 100 FIT. Bayesian inference is applied to update posterior probabilities, and the results indicate that perception system failures (46.06 FIT) are the most significant contributor, particularly failures to detect existing objects (PF5) and misclassification (PF6). Mitigation strategies are proposed for sensors, perception, decision-making, and motion control to reduce the collision risk. The FTA-BN integration approach provides dynamic risk quantification, offering system designers refined failure rate targets to improve AV safety.

<div class="htmlview paragraph">Fault tree analysis (FTA) evolved from the aerospace industry in the 1960’s. A fault tree is deductive logic model that is generated with a top undesired event in mind. FTA answers the question, “how can something occur?” as opposed to failure modes and effects analysis (FMEA) that is inductive and answers the question, “what if?” FTA is used in risk, reliability and safety assessments. FTA is currently being used by several industries such as nuclear power and chemical processing. Typically the automotive industries uses failure modes and effects analysis (FMEA) such as design FMEAs and process FMEAs. The use of FTA has spread to the automotive industry. This paper discusses the use of FTA for automotive applications. With the addition automotive electronics for various applications in systems such as engine/power control, cruise control and braking/traction, FTA is well suited to address failure modes within these systems. FTA can determine the importance of these failure modes from various perspectives such as cost, reliability and safety. A fault tree analysis of a car starting system is presented as an example.</div>

In this paper, transportation problem of hazardous materials (hazmat) by various transportation modes is considered. Accident probabilities related to human and ecologic risks are assumed for each transportation mode including road, rail, and marine modes. Thus, a two-stage approach following the fault tree analysis (FTA) first, a mathematical modeling approach second is provided. In the first stage, FTA is employed to analyze the risks for each transportation mode. In the second stage, a mixed integer nonlinear programing model is formulated by using the results of the FTA to minimize the transportation cost and risk cost for each mode. Computational results for the instances in the literature and generated instances are provided. Results show that the two-stage approach is able to produce high-quality solutions. Most effective parameter is human-based possibility with 82% for the road accident, vehicle-based possibility with 64% for the rail accident, material-based possibility with 76% for the marine accident during the hazmat transportation.

The Italian construction sector suffered more than the other industrial sector the negative effects of the world economic crisis. A consequence has been a change in its organization concerning labour and managent on the health and safety issues. Evidence of this can be found in an increase of fatal accidents and occupational diseases. In particular occupational diseases became a health concern in construction. The aim of the presented research work is to assess the hazard of asbestos fibres inhalation due to decommissing of old industrial promises with roofing of asbestos. The developed approach starts from a probabilistic assessment of the risk, using the Risk Analysis tools, the Fault Tree Analysis (FTA) and the Failure Mode and Effect Analysis (FMEA). Risk assessment through the FTA is based on a backward approach: it starts from the malfunction of the system, called Top Event, in order to look for the triggering causes (events); on the contrary, the FMEA analysis is based on a forward approach: they start from a set of events and go on in order to analyse what kind of malfunctioning these can generate in the system. Both the FTA and the FMEA are applied to three approved decontamination techniques (encapsulation, confinement and removal), and for each of them operational phases, equipment, building products and PPE (Personal Protective Equipment) are evaluated highlighting the procedures needed to operate in complete safety. With this approach the main factors that contribute to the increase of the risk of asbestos fibres inhalation are detected: preliminary analysis and assessment of the state of building decay, the use and maintence of PPE and machinery, and the supervision during main working phases.