Fault Tree Research Articles

Risk Analysis of Equipment Loss During Marine Survey Operation by Integrating Fault Tree to Bayesian Network

Risk Analysis of Equipment Loss During Marine Survey Operation by Integrating Fault Tree to Bayesian Network

Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels

To evaluate the earthquake disaster resistance capability of highway tunnels, it is necessary to study an index system for the earthquake disaster resistance capability of highway tunnels. This paper uses a literature research method. The damage of highway tunnels in 12 earthquake disasters recorded in detail in China and abroad was investigated. According to the types of hazard-beating bodies and damage forms, 17 seismic damage scenes of highway tunnels are classified. According to different damage scenes, the fault tree analysis model is used to identify the damage-causing factors. Combined with the industry standards, norms and research results related to the earthquake resistance of highway tunnels, the evaluation indices are analyzed from three perspectives: engineering geological factors, tunnel structural factors and operation management factors. According to the principle of index system construction, a four-level index system is constructed, which takes the earthquake disaster resistance capability of highway tunnels as the target layer; engineering geological factors, tunnel structural factors and operation management factors as the criterion layer indices; 12 indicators as the index layer and 35 specific indicators. The established index system for the earthquake disaster resistance capability of highway tunnels has strong scientific, rational and application value and can provide a reference for the evaluation of the earthquake disaster resistance capability of highway tunnels and can also provide important support for the safe operation and sustainable development of highway tunnels.

Quantitive HAZOP and D-S evidence theory-fault tree analysis approach to predict fire and explosion risk in inert gas system on-board tanker ship

Tanker ships carry volatile cargo, presenting inherent risks of fire and explosion. Inert gas systems (IGS) are pivotal in mitigating risks by displacing oxygen in cargo tanks. However, failure of IGS components may lead to fatal consequences such as loss of life and marine pollution. This paper prompts a systematic approach integrating Quantitative Hazard and Operability (HAZOP) analysis under D-S (Dempster–Shafer) evidence theory and Fault Tree Analysis (FTA) to predict and quantify the fire and explosion risk associated with IGS malfunction on tanker ships. The methodology systematically evaluates failure probabilities, and consequences using HAZOP to identify critical scenarios. D-S evidence theory is employed to address uncertainties and incorporate expert knowledge into the analysis. FTA is applied to model fault propagation and assesses the likelihood of fire and explosion events based on the identified failure scenarios. A case study is presented to demonstrate the application of the proposed methodology, illustrating effectiveness in identifying high-risk scenarios and providing insights for enhancing operational safety minimising the risk of IGS on tanker ships. The findings show that the risk of fire and explosion in the inert gas system due to the high concentration of oxygen entering the tank was found to be 2.86E-01. Besides its robust theoretical background, the findings of the paper provide the utmost contribution to ship crew, ship inspectors, HSEQ managers and safety professionals for proactive risk mitigation strategies, contributing to the advancement of safety management practices in the maritime industry.

A Step-by-Step Methodology for Obtaining the Reliability of Building Microgrids Using Fault TreeAnalysis

This paper introduces an improved methodology designed to address a practical deficit of existing methodologies by incorporating circuit-level analysis in the assessment of building microgrid reliability. The scientific problem at hand involves devising a systematic approach that integrates circuit modeling, Probability Density Function (PDF) selection, formulation of reliability functions, and Fault Tree Analysis (FTA) tailored specifically for the distinctive features of building microgrids. This method entails analyzing inter-component relationships to gain comprehensive insights into system behavior. By harnessing the circuit models and theoretical framework proposed herein, precise estimations of microgrid failure rates can be attained. To complement this approach, we propose a thorough investigation utilizing reliability curves and importance measures, providing valuable insights into individual device failure probabilities over time. Such time-based analysis plays a crucial role in proactively identifying potential failures and facilitating efficient maintenance planning for microgrid devices. We demonstrate the application of this methodology to the University of Antioquia (UdeA) Microgrid, a low-voltage system comprising critical components such as solar panels, microinverters, inverters/chargers, batteries, and charge controllers.

Pengendalian Bahaya dan Risiko K3 Menggunakan Metode HIRADC dan FTA Pada Industri Kerupuk

CV Irma is one of the traditional cracker processing industries located in the city of Bekasi. The production process of processing these crackers is still carried out traditionally. This can cause gaps in the occurrence of work accidents due to processes and ways of working that still use manual processes. The purpose of this study is to provide proposals for K3 risk control to minimize the potential dangers and risks that exist in the cracker production process. The study was conducted using the HIRADC (Hazard Identifcation, Risk Assessment and Determining Control) and FTA (fault tree analysis) methods to determine the root cause of a kerrja accident with a high risk value. The results of this study were obtained to be able to reduce the risk level of the existing potential hazards, from the high, medium, and low value risk levels from the comparison before and after control.

A reliability analysis method for fuzzy multi-state system with common cause failure based on improved the weakest T-norm

Recently, the reliability analysis of complex engineering systems has been confronted with a range of challenges, including fuzziness, multiple states, and common cause failures. In particular, factors such as changes in the working environment and human error contribute to the fuzzy probability value for each state in the system, which brings great challenges to its reliability analysis. In this paper, a new reliability analysis method for fuzzy multi-state system with common cause failure is proposed, which developed the fault tree analysis method and fuzzy set theory. First, a model integrating common cause failure with fuzzy T-S fault tree analysis model has been illustrated, and the equivalent model is given. Meanwhile, a novel fuzzy arithmetic method based on the weakest n-dimension T-norm is designed to solve the proposed model. Finally, the aircraft power system is analyzed as an example to verify the effectiveness of the proposed method. The result shows that the proposed method effectively enhances the capacity of failure tree analysis in assessing the reliability of complex systems characterized by multiple states, fuzziness, and common cause failures.

Uncovering the Role and Labor of Volunteer Leaders within a Community-Led Urban Tree Planting Initiative

Urban forestry programs across North America rely on volunteers to lead urban forest stewardship activities and encourage participation in tree planting and general maintenance. We examined the leadership structure of a tree planting initiative (TPI) in the greater Philadelphia region to understand the role and labor contributions of volunteer leaders, and the current challenges of achieving equitable urban tree canopy within community-led TPIs. Using an online survey, we found that leaders contribute unpaid labor, time, and skills within the following categories: group organization and communication, tree-planting and event coordination, tree care and maintenance, and broader environmental education and advocacy. Volunteer leaders also face challenges related to leadership and administrative tasks. This study deepens understandings of the labor needs within volunteer TPIs and potential ways to support the equitable growth of these initiatives moving forward. Future research is needed to understand recognitional equity gaps in volunteer leadership more broadly.

NaTech triggered by lightning: Novel insights from past events in the process industry

Lightning strikes, a prominent meteorological event, pose a significant risk of triggering technological disruptions within the process industry. To better understand this phenomenon, an analysis focused on past lightning-triggered events was carried out, examining open-source industrial-accident databases to compile a new NaTech-driven dataset of 689 records. First, an overall quantitative analysis revealed that over 80 % of these events involved incidents or loss of containment. Notably, 83.3 % of them occurred during the spring and summer, indicating a seasonal pattern. Based on the frequency of functional attributes, the chemical and petrochemical macro-sector was the most vulnerable, followed by storage and warehousing. About 40 % of all classifiable events happened on storage equipment, while 21 % happened on electric and electronic devices. Given the lack of valuable information for the principal source of data (NRC), the technological scenarios triggered were characterized using a refined subset of 127 observations, obtained considering the “other sources” of data. Fire scenarios predominated at 56 %; coincidentally, roughly 70 % of all scenarios involved hazardous substances classified as physical hazards. Estimated losses for the available information underscored the adverse consequences of lightning-triggered NaTech events, highlighting their major impact on both safety and the environment. An analysis of the event tree showed the logical path from the lightning strike to the final ignition scenarios (considering a subset of 107 records). This path accounted for 36 % of the classifiable records that directly affected the structure, while more than 50 % of them did not. Bayesian network structures made it possible to get conditional probabilities from the event tree and improved the model by adding attributes for vulnerable equipment and macro-sectors. In order to deal with the uncertain data, algorithms were used to generalize the models that were obtained from smaller subsets of data with more accurate information to the whole dataset. It provides an important additional view of unclassifiable data that otherwise remained in the dark. This novel insight contributes to increase the vulnerability awareness of industrial assets against lightning strikes.

Evidence‐based fault tree analysis of the hydraulic system in CNC machine tools

AbstractThe hydraulic system is an integral part of CNC machine tools. In analyzing the reliability of machine tool hydraulic systems, their failures are influenced by both aleatory and epistemic uncertainties. This paper utilizes the fault tree analysis method to address failure modes subject to epistemic uncertainty, using the interval rough number scoring method to evaluate the probability of such failures occurring. The resulting reliability calculation is termed as “subjective reliability”. For failure modes influenced by aleatory uncertainty, objective data combined with the Dempster–Shafer evidence theory is used to determine their failure probability, with the corresponding reliability calculation referred to as “objective reliability”. Finally, a comprehensive calculation of both subjective and objective reliability is conducted to determine the overall reliability of the hydraulic system, along with the ranking of the importance of basic events of fault tree. This methodology covers scenarios with small samples, sufficient data, and their combinations, offering extensive application prospects.

Reliability modeling and assessment of a community microgrid with electric vehicle charging station as a critical load

Microgrids are constituted of interconnected loads and dispersed energy resources. They increase the reliability of the power supply, decrease the cost of the per unit energy and support the green environment concept. Microgrids have gained importance due to their ability to deliver reliable energy and operate in both grid-connected and off-grid/island modes. Renewable energy sources such as photovoltaic (PV) and wind can be connected to the microgrid system to generate green energy. An electric vehicle charging station (EVCS) can be deployed in microgrids powered by renewable energy sources. An effective way to handle the rapidly rising load demand of electric vehicles (EVs) is to integrate EV recharge stations into microgrids. Due to the intermittent nature of solar PV and EV load's reliability, an evaluation of microgrids becomes challenging. Assessment of the components’ failure and their impact on the system reliability is proposed using different methods. Reliability Block Diagram (RBD), Markov Reliability Modeling (MRM) and Fault Tree Analysis (FTA) - Monte Carlo simulation are used for the Reliability Modeling of a community microgrid considering EVCS as a critical load. The mean time to failure (MTTF) is computed for the entire microgrid which includes PV, wind and fuel cell systems integrated into the grid with associated power electronic devices, transmission lines, transformers and point of common coupling (PCC). MATLAB software was used to program and study the reliability aspects of the microgrid. Using the FTA MCS technique and grid functions for 45 years with 20 % reliability, significant results were obtained in terms of a MTTF rate of 26.16 years.

Reliability assessment of bridge hangers based on fault tree with response surface

Bridge hangers play a crucial role in transmitting forces and supporting bridge structures. The condition of these hangers directly impacts the overall safety of the bridge. However, they are susceptible to fatigue failure due to cyclic loads like wind and vehicle traffic. Therefore, evaluating the fatigue performance of bridge hangers is vital. Typically made up of multiple parallel steel wires, the failure of even one wire can affect the fatigue performance of the entire hanger. This underscores the importance of considering the individual components’ impact on the system as a whole. This study presents a time-dependent reliability assessment model for bridge hangers based on the S-N curve of steel wire. Additionally, a system-level reliability assessment model is developed using fault tree analysis, taking into account the influence of broken wires and stress redistribution on the hanger system. The model is solved using response surface methodology, and through five numerical simulations, the study examines the effect of key parameters, including the number of broken wires, equivalent structural stress, and total cycles on the system’s reliability.

Fault tree analysis for risk assessment of rocket engine testing process at high altitude test facility

Fault tree analysis for risk assessment of rocket engine testing process at high altitude test facility

Dynamic human error risk assessment of group decision-making in extreme cooperative scenario

For groups in extreme environments, their reliability requirements in complex operational systems are high due to isolation from society, physical constraints for extended periods of time, and exposure to significant hazards. This study attempts to solve the problem of dynamic quantitative risk analysis of decision-making process for extreme group during complex cooperative scenarios. Based on an extended IDA team cognitive model, typical failure modes and performance quantification mechanisms of team communication were analyzed. The performance curve was utilized to distinctively model the cognitive behaviors of operators within the team, driving the cognitive transmission paths of group decision-making in a hybrid event tree. Error calibration of human error due to time uncertainty was conducted. The steps and techniques for dynamic risk analysis of decision-making of extreme groups under complex, dynamic, and ambiguous scenarios were summarized. The proposed method was validated through simulation in a typical powered diving task of manned submarine.

Integrating security in hazard analysis using STPA-Sec and GSPN: A case study of automatic emergency braking system

Hazard analysis is a vital step in developing intelligent connected vehicles, aiming to eliminate or control hazards in the initial stages of system development and to provide theoretical support for the system’s safety design. However, conventional hazard analysis methods, such as Fault Tree Analysis and Failure Mode and Effects Analysis, suffer from two shortcomings: they do not account for the impact of cybersecurity factors on system safety and do not provide sufficient quantification of hazard scenarios. To this end, we propose a quantifiable hazard analysis method with security consideration, which integrates System Theoretic Process Analysis for Security (STPA-Sec) and Generalized Stochastic Petri Net (GSPN), supporting the extraction, modeling, and quantification of hazards. Specifically, we employ STPA-Sec for qualitative analysis to identify causal scenarios, safety requirements, security requirements, and the corresponding mitigations. Then, based on the identified causal scenarios, a GSPN model is established to quantify system-level hazards. A case study on a real open-source test vehicle demonstrates that the proposed method not only offers a comprehensive analysis of hazards but also provides a quantitative assessment. Comparative assessments suggest that the proposed method exhibits an advantage in terms of analysis processes (integrating security) and results (quantification).

Knowledge-based system and expectation-maximization to discovering causes of imperfect labels in vehicular networks clustering

This paper focuses on the development of a knowledge-based system for automatically diagnosing issues in Vehicular ad hoc networks (VANETs). VANETs enable communication between vehicles and infrastructure, enhancing road safety and efficiency through timely information exchange. The proposed system aims to efficiently maintain and ensure the continuity of network service by leveraging innovative pattern recognition methods tailored to VANETs. The automatic diagnosis problem in VANETs involves estimating the operating class of network components based on sensor observations. This entails associating sensor measurements with specific operating modes. By implementing condition-based preventive maintenance procedures, potential component failures can be detected early, mitigating network disruptions. Various approaches, such as expert systems, fault trees, network state models, and statistical learning through pattern recognition, can be employed to address this problem. This paper primarily focuses on the statistical learning approach, where a classification or regression function is learned from a set of examples to assign operation modes to new measurements. It discusses relevant metrics and preprocessing techniques to simplify the decision-making process. The diagnostic system's results are determined based on the formulation of the classification or regression problem. The learning base is constructed, and an appropriate classification method is selected to develop and validate the automatic diagnosis system. While non-parametric models like support vector machines are commonly used, this article emphasizes the significance of considering assumptions and leveraging additional information to enhance performance. It proposes a more specific formalization of the problem, integrating the unique characteristics of VANETs. The contributions of this article revolve around the theory of belief functions, a generative approach, and the utilization of parametric models defined using graphical models. Experimental studies conducted on artificial datasets have demonstrated the benefits of the semi-supervised approach within the context of VANET networks.

Reliability and Detectability of Emergency Management Systems in Smart Cities under Common Cause Failures.

Urban areas are undergoing significant changes with the rise of smart cities, with technology transforming how cities develop through enhanced connectivity and data-driven services. However, these advancements also bring new challenges, especially in dealing with urban emergencies that can disrupt city life and infrastructure. The emergency management systems have become crucial elements for enabling cities to better handle urban emergencies, although ensuring the reliability and detectability of such system remains critical. This article introduces a new method to perform reliability and detectability assessments. By using Fault Tree Markov chain models, this article evaluates their performance under extreme conditions, providing valuable insights for designing and operating urban emergency systems. These analyses fill a gap in the existing research, offering a comprehensive understanding of emergency management systems functionality in complex urban settings.

ARMA time series prediction model for fault detection of launch vehicle

Spaceflight is a high-risk activity, especially for launch vehicles, and the losses caused by launch failures are considerable. Real-time fault detection is a prerequisite to ensure that future launch vehicles can detect and isolate faults in a timely manner, and ultimately reduce the hazard of fault. In this article, a time series prediction model of axial overload and three-axis angular rate is established using ARMA (Auto Regressive Moving Average) time series prediction model, and the residuals of the prediction model are analyzed and combined with the fault determination decision strategy to determine whether the system has a fault, and a fault tree can be built to further locate the fault location.

A hybrid approach of partially applying BDD for seismic PSA quantification

The binary decision diagram (BDD) method provides a means to calculate the exact probability of a fault tree, but cannot solve large fault trees for nuclear power plant PSAs. However, the BDD method can be used as a supplementary means to increase the accuracy of PSA quantification, especially for a seismic PSA which includes many non-rare events.Since it is difficult to solve a large PSA model using BDD method, several approaches have been developed that partially apply the BDD technique to small-sized branches of a large PSA model. This paper proposes an approach of partially applying BDD technique to large-sized branches. The results from the pilot application to a seismic PSA model shows that the partial BDD approach of this paper provides a relatively accurate way to quantify each sequence as well as the overall core damage in a seismic PSA model.

Multi-scale systematization of damage and failure modes of composite cryogenic hydrogen vessels according to the Fault Tree method

Achieving a safe and lightweight design of composite liquid hydrogen (LH2) vessels without much empirical data requires advanced reliability assessment. An approach based on the Fault Tree method is proposed, to allow the systematization and modelling of functional and structural failure modes and their interactions. A hierarchical system model spanning across the length scales of the LH2 vessel down to the composite material is established. Subsequently, the failure probability of the composite material is estimated in dependence on temperature and load case using Weibull analysis. Using the accident data of US Air Carriers operating under 14 CFR 121 between 2012 and 2019, the probabilities of the load cases are derived, allowing a quantitative reliability analysis of the selected failure modes. Requirements for material and system design are derived in dependence of the maximum allowable failure probability.

Analysis of the valve positioner pilot valve hole plugging

The valve positioner is an important supporting component of the regulating valve. The pilot valve is a crucial component of the valve positioner, and its major job is to reverse the gas route. The current lack of efficient solutions for internal pilot valve blockage is due to the unavoidable oil loss inside the positioner and the simple entry of external dust and other contaminants into the valve, resulting in oil blockage inside the pilot valve. The fault tree theory is utilized in this study to evaluate the reason of the pilot valve blockage problem, and it is coupled with 39 engineering factors in TRIZ theory to innovate the internal structure of the pilot valve using a contradiction matrix and 40 invention principles. The product’s dependability was then confirmed by fluid analysis using Ansys fluent.