System Fault Tree Research Articles

Fuzzy reliability algorithm for the shutdown system of research reactor

Abstract Probabilistic safety assessment (PSA) has been applied to evaluate the safety of nuclear reactor systems. The results of PSA are used by designers, utilities and regulatory body to confirm the reactor design, to change operation procedures, to enhance the reliability of safety systems, or to modify regulation. Fault tree analysis (FTA) has been used as a deductive means for PSA. System components are assumed to always have defined failure probabilities; however, in reality this assumption is questionable and sometimes there is insufficient data about the failure probability of some components. To solve this problem, fuzzy approaches have been proposed and implemented. The aim of this study is to apply a fuzzy reliability algorithm to estimate basic events failure probabilities for the shutdown system of a research reactor. The reactor is a 22 MW light water open pool type material test reactor and the shutdown system is responsible for the fast shutdown of the reactor whenever safety limits are exceeded. Then, to illustrate the ability of the algorithm, the results are compared with real basic event failure probabilities. The results verify the competence of the algorithm and confirm that it can be applied as an alternative method when quantitative data are not available. Also, a risk importance measure is done on the fault tree of the shutdown system to illustrate which components need to be focused for improvements to achieve the safe operation of shutdown system.

Bayesian network approach for dynamic fault tree with common cause failures and interval uncertainty parameters

Traditional fault tree analysis often assumes that the basic events are independent and the failure parameters are known. Therefore, it is powerless to deal with the correlation among basic events and the uncertainty of failure parameters due to the small failure data. Therefore, a framework based on continuous-time Bayesian network is proposed to evaluate the reliability of fault tree with common cause failures (CCF) and uncertainty parameters. Firstly, the best-worst method (BWM) and hesitant fuzzy set (HFS) are introduced to address the issue of β-factor being influenced by experts’ subjectivity. Then, the interval theory is introduced to deal with the uncertainty parameters. Based on continuous-time Bayesian network, the conditional probability functions of logic gates (i.e. AND gate, OR gate, spare gate, priority AND gate) with CCF are derived, and the upper and lower bounds of failure probability of top event can be solved. Finally, the fault tree of CPU system is given to verify the effectiveness of the proposed framework.

Situation modeling and evaluation for complex systems: A case study of satellite attitude control system

Because the dependence, competition, correlation, and other complex interactions among the components and between system and environment, the complex system is difficult to be modeled directly. As an important spacecraft system, satellite needs to meet the high reliability demand. However, the increasingly complex satellite system structure and complex space environment interference make the on-orbit satellite failure inevitable. Modeling and evaluation of satellite conditions can identify potential problems of key system components in time. Firstly, in order to accurately describe the system components and measure the reliability, the correlative damage coefficient is introduced to describe the component degradation process, and the component reliability model of the satellite attitude control system is established. Then, to solve the problem of large state space and changeable condition of system reliability model, the concept of tangent order pair is introduced into the multi-state dynamic fault tree of system. The depth-first search algorithm is used to sort the underlying event components of the fault tree, and the multi-state multi-value decision graph model of different state events is constructed. The minimum cut set of the model is obtained to evaluate the system state. The experimental results show that the proposed method can effectively simulate the system situation under different fault modes, effectively reduce the state space of the system reliability model, and improve the convergence and accuracy of the system fault probability evaluation.

Unavailability Assessment Based on Improved-Dependent Uncertain Ordered Weighted Averaging Operator and Fault Tree Analysis

The fire-fighting system is one of the proactive technical barriers related to liquefied petroleum gas storage tank safety. This paper presents an integrated approach that uses fuzzy set theory, an improved-dependent uncertain ordered weighted averaging operator and fault tree analysis to handle uncertainty in the unavailability assessment of fire-fighting systems. In this study, the center of area is used to defuzzify triangular fuzzy numbers. Furthermore, for the fire-fighting system fault tree, importance analysis, including Fussell–Vesely importance measure and risk reduction worth of basic events, are performed to identify the weak links of the fire-fighting system. In addition, a real case study on a fire-fighting system for a liquefied petroleum gas storage system in an LPG unit in Algeria is provided to illustrate the effectiveness of the proposed methodology. The research results show that the proposed methodology makes it possible to assess the unavailability of the entire system by analyzing weak links. Consequently, some suggestions are given to take preventive–corrective actions in advance, in order to reduce the failure probability of fire-fighting system and assist the practitioners in setting priorities for improving safety procedures in liquefied petroleum gas storage tanks. The study provides references for analyzing safety barriers in a complex system.

Assessing building's post-earthquake functional recovery accounting for utility system disruption

Experience from past earthquakes has demonstrated the need to account for design goals beyond safety, known as functional recovery objectives, in the interest of community resilience. Frameworks have been proposed in the literature to assess the post-earthquake functional recovery of a building, but without accounting for utility systems’ disruption, which may be a key contributor to determining when a building is functional. This paper integrates a previously proposed probabilistic method for estimating the post-earthquake restoration of critical utility services with an individual building's functional recovery assessment framework. The integration was performed by incorporating utilities into the building system fault trees embedded into a functional recovery framework for various building occupancies (residential and commercial office buildings). Once incorporated, the results are used to interrogate the functional recovery of a reinforced concrete building, and the recovery time results were presented for seven cases investigating contributing factors in the functional recovery results including the number of crews available for lifeline restoration, the effect of low-quality service on meeting tenant requirements for elevators, heating ventilation and air conditioning (HVAC), plumbing and electrical systems, consideration of fire watch, the effect of building seismic retrofit, as well as different cases of fragility functions for the lifeline systems. Results showed that utility systems’ disruption does not have a significant impact on the recoccupancy of a building because only one utility-dependent building system (fire suppression) is needed for the building's safety. Unlike reoccupancy, utility systems are significant for functional recovery, mainly at moderate hazard levels because, at these levels, lifeline networks could be damaged without significant building damage, such that the lifeline systems restoration governs. Buildings with more restrictive tenant requirements are more sensitive to tenant disruptions.

Research on static fault tree analysis method for inerting system safety based on random number generation

PurposeTo reduce the flammability exposure assessment time and meet the requirements of airworthiness regulations of transport aircraft, inerting system has become the standard configuration of modern civil aircraft. Therefore, airworthiness regulations put forward definite quantitative index requirements for the safety of inerting system, and to obtain the quantitative data of the safety of inerting system, it is necessary to solve the calculation method. As one of the quantitative/qualitative evaluation techniques for system safety, fault tree analysis is recognized by international airworthiness organizations and national airworthiness certification agencies. When fault tree analysis technology is applied to quantitative analysis of the safety of inerted system, there are still some problems, such as heavy margin of constructing fault tree, great difficulty, high requirement for analysts and poor accuracy of solving when there are too many minimum cut sets. However, based on tens of thousands of flight simulation tests, Monte Carlo random number generation method can solve this problem.Design/methodology/approachIn this paper, the fault tree of airborne inerting system is established, and the top event is airborne inerting system losing air separation function. Monte Carlo method based on random number generation is used to carry out system security analysis. The reliability of this method is verified.FindingsThe static fault tree analysis method based on Monte Carlo random number generation can not only solve the problem of quantitative analysis of inerting system, but can also avoid the defects of complicated solution and inaccurate solution caused by the large number of minimum cut sets, and its calculation results have good reliability.Practical implicationsThe research results of this paper can be used as supporting evidence for airworthiness compliance of airborne inerting system.Originality/valueThe research results of this paper can provide practical guidance for the current civil airworthiness certification work.

Reliability evaluation of electromechanical braking system of mine hoist based on fault tree analysis and Bayesian network

Electromechanical braking system is the key way to improve the braking response ability of mine hoist. At present, the reliability research of electromechanical braking system is less. In order to further analyze and improve the reliability of electro-mechanical braking system, this paper adopts the reliability analysis method of electro-mechanical braking system based on fault tree and Bayesian network. Firstly, the fault tree of the electro-mechanical braking system is established, and then the fault tree is transformed into a Bayesian network, and the posterior probability, probability importance and key importance of each root node are inversely deduced. The diagnosis results show that the ball screw is the weakest link of the electro-mechanical braking system. Then the static simulation and fatigue life simulation of the ball screw are carried out for optimization, and the optimal model of the ball screw is determined. Finally, the electro-mechanical brake installed with the optimized ball screw is tested and analyzed. After the reliable performance test of the electro-mechanical brake, it is finally determined that the braking effect of the optimized electro-mechanical brake is stable.

Data-driven extraction and analysis of repairable fault trees from time series data

Fault tree analysis is a probability-based technique for estimating the risk of an undesired top event, typically a system failure. Traditionally, building a fault tree requires involvement of knowledgeable experts from different fields, relevant for the system under study. Nowadays’ systems, however, integrate numerous Internet of Things (IoT) devices and are able to generate large amounts of data that can be utilized to extract fault trees that reflect the true fault-related behavior of the corresponding systems. This is especially relevant as systems typically change their behaviors during their lifetimes, rendering initial fault trees obsolete. For this reason, we are interested in extracting fault trees from data that is generated from systems during their lifetimes. We present DDFTAnb algorithm for learning fault trees of systems using time series data from observed faults, enhanced with Naïve Bayes classifiers for estimating the future fault-related behavior of the system for unobserved combinations of basic events, where the state of the top event is unknown. Our proposed algorithm extracts repairable fault trees from multinomial time series data, classifies the top event for the unseen combinations of basic events, and then uses proxel-based simulation to estimate the system’s reliability. We, furthermore, assess the sensitivity of our algorithm to different percentages of data availabilities. Results indicate DDFTAnb’s high performance for low levels of data availability, however, when there are sufficient or high amounts of data, there is no need for classifying the top event.

Integrating α-cut interval based fuzzy fault tree analysis with Bayesian network for criticality analysis of submarine pipeline leakage: A novel approach

Submarine pipelines are the major transportation mode of marine oil and gas resources. Because of submarine pipeline damage, the leakage of oil and gas will result the serious consequences such as environmental disasters, fires and explosions, and huge economic losses. There are variously internal and external factors that initiate spill accidents of oil and gas. To prevent and mitigate such accidents, risk analysis is an efficient way. Fault tree analysis is an effective tool to identify failure causes and perform the risk assessment. In fault tree analysis, it is presumed that all basic events are statistically independent and have precise occurrence probabilities. In the case, when probability data of basic events of system fault tree are unavailable or imprecise, the concept of fuzzy set theory and expert elicitation is used to obtain qualitative data. In the quantification of qualitative data, experts‘ knowledge is used which may raise issues such as incompleteness, imprecision, and lack of consensus. In the process to minimize the uncertainty, expert’s opinions are aggregated and updated to the posterior possibilities using the prior observations. Bayesian networks have the advantages of representing the dependencies of events, updating probabilities, and dealing with uncertainties. In this research paper, a novel methodology is proposed by combining fuzzy fault tree analysis and Bayesian network to obtain updated prior possibilities of basic events and top event of system fault tree when new information are available. The main contributions of this research are: weakest t-norm based arithmetic operations on fuzzy numbers are employed for less uncertainty accumulation during the process; weakest t-norm and α-cut based similarity aggregation method is developed to evaluate the possibilities of basic events and top event in system fuzzy fault tree analysis; the obtained prior possibilities are then updated using fuzzy Bayesian network; criticality analysis is executed using the posterior possibilities of basic events and top event. Further, a case study of leakage in submarine pipeline is discussed to demonstrate the applicability and effectiveness of the proposed methodology. The obtained results are then compared with the pre-existing results which shows the validity and applicability of the proposed method.

Reliability Analysis of the High-Voltage Power Battery System Based on the Polymorphic Fuzzy Fault Tree

Accidents caused by the failure of high-voltage power battery systems are rising with the increase of pure electric commercial vehicles. The fault tree analysis method based on traditional reliability is no longer suitable for quantitative evaluation of polymorphic systems. In this paper, the polymorphic fuzzy fault tree of the high-voltage power battery system for pure electric commercial vehicles is established and analyzed qualitatively and quantitatively based on a combined theory of the polymorphic theory, fuzzy mathematical theory, group decision theory, and fault tree analysis theory. The results showed that the multistate reliability-analysis method of the fuzzy fault tree could describe the various fault states of the high-voltage power battery system. Through quantitative evaluation of the reliability of system, the low-temperature environment and CAN high and low reverse connection were the weakest links of the system, and the problem of the occurrence probability of each state of the unknown polymorphic bottom event in the sub-fault tree of the deteriorated-state mode was solved quickly using group decision-making to deal with fuzzy probability. It provides theoretical reference for system design and detection process, which has important practical significance for the improvement of high-voltage power battery system.

Fuzzy fault tree analysis of chlorine gas release hazard in Chlor-Alkali industry using [formula omitted]-cut interval-based similarity aggregation method

In chemical industries, the release of toxic gases and chemicals is always a serious concern as it has the potential to harm human life and the environment, leading to significant losses. Risk assessment in chemical industries is essential to identify and prevent such type of unwanted hazards. Conventional fault tree analysis has been used effectively to identify the system failure causes and evaluate the system reliability, but it requires quantitative historical failure data of system components. In many real-world applications, it can be difficult to obtain precise and sufficient quantitative failure data of system components. This research paper proposes a novel fuzzy set theory-based fault tree analysis to evaluate system failure probability by utilizing the available qualitative data such as expert opinions/views when quantitative historical failure data of system components is unavailable or insufficient. To aggregate the different opinions of selected experts, a new similarity aggregation method is developed by using interval distance-based similarity measure function. The proposed fuzzy fault tree analysis applies system fault tree, α-cut intervals of fuzzy membership functions, and interval arithmetic operations. To validate the applicability and effectiveness of proposed approach, the basic event probabilities in fault tree analysis of chlorine release from storage and filling facility are generated using proposed approach and then compared with the known probabilities. Further, importance analysis is performed to evaluate the contribution of each basic event in top event “chlorine release” occurrence that would help the decision makers to identify the risk factor of chlorine release. The obtained results may be helpful for decision makers to decide whether and where to take protective actions in the risk management process.

Failure analysis of scraper conveyor based on fault tree and optimal design of new scraper with polyurethane material

The scraper conveyor is an important equipment in modern coal mining process. Traditional alloy steel scrapers, as one of the main components of the scraper conveyor, are disadvantageous in that they are not only vulnerable to failures such as fracture and wear, but also take up over 35% of the output power to rotate themselves, resulting in excessive idle load. In order to improve the running state of the scraper and the overall loading of the scraper conveyor and extend the service life of the scraper, a transportation system fault tree of the scraper conveyor was drawn based on fault tree analysis. The minimum cut set of the fault tree was calculated. The Fussel–Vesely importance (FV) of scraper failure to transportation system was 0.458. Failure of the original scraper primarily includes wear and fatigue, as result from the excessive wear caused by the sliding friction between the scraper and the middle trough. A new optimized scraper design was developed: the support was forged from PU material to reduce self-weight and increase buffer capacity; the scrape was restructured to include rolling elements and a detachable support, which transformed sliding friction to rolling friction. Use of the new scraper in a mine reduced the idling rate of the scraper conveyor to 23% and increased the scraper service life by around 3 times. The research result of this paper could provide reference for further application of lean design theory in the updating of coal mining equipment.

Reliability Analysis of Metro Door System Based on Fuzzy Multi-State Bayesian Network

Considering the shortcomings of the fault tree analysis (FTA) method in the reliability analysis of metro door systems, Bayesian network (BN) and fuzzy theory were introduced to establish the failure probability model of a metro door system. A fault tree of the metro door system was established based on the structure of the metro door, the operation data record and the practical experience of relevant engineers. The BN of the metro door system was constructed based on the fault tree. For the problem that the prior probabilities of root nodes with missing data were unavailable, fuzzy theory was introduced to convert the expert language values on these missing data nodes to corresponding prior probabilities, which were substituted into the BN along with the root nodes whose prior probabilities were obtained from the operation fault data to calculate the leaf node probability. Cause analysis of the metro door system was performed with bi-directional reasoning of BN, which provided a way to find the key factors that caused door faults and the metro door system fault probabilities.

Rapid Fault Diagnosis Method of Elevator System Based on Multiattribute Decision Making

Fault tree analysis is often used in elevator fault diagnosis because of its simplicity and reliability. However, the traditional fault tree method has the problems of low efficiency due to ignoring location change of bottom events during troubleshooting. This paper proposes a rapid diagnosis method based on multiattribute decision making to solve the problem. The fault tree of the elevator system is constructed based on expert knowledge and multisource data, and the location-related matrix is constructed according to the complex vertical structure of the elevator. Then, the attributes of bottom events such as the failure probability, search cost, location time cost, and location-related attributes are comprehensively analyzed in this paper. Finally, the TOPSIS method for dynamic attributes is used based on the work above to achieve the optimal troubleshooting sequence of elevator vibration fault. The results show that the proposed method is more efficient when compared to the optimal troubleshooting sequence obtained by the traditional method.

Analysis of comprehensive sensitivity evaluation of aircraft system based on FTA-AHP

Aiming at the lack of unified evaluation index for the sensitivity analysis of fault tree of the system, the Analytic Hierarchy Process(AHP) is introduced into Fault Tree Analysis(FTA), and an FTA-AHP method is proposed to generate the comprehensive sensitivity evaluation index of the system. Firstly, the reliability analysis model of the system fault tree is established, and three indexes including probabilistic sensitivity, relative sensitivity and structural sensitivity, are calculated by using the traditional sensitivity analysis method. Then, combined with the analytic hierarchy process, the weight coefficients were determined according to the sensitivity analysis results above and the type of the minimum cut set, and the comprehensive sensitivity evaluation and analysis of the system fault tree were carried out. Finally, the case verification shows that compared with the traditional method, the comprehensive sensitivity ranking results have less repeatability, and the factors are considered comprehensively, which is convenient for decision makers to make judgment. The synthetic sensitivity evaluation model developed provides support for system reliability analysis.

Applications of FFTA–HFACS for Analyzing Human and Organization Factors in Electric Misoperation Accidents

Human and organizational factors (HOFs) play an important role in electric misoperation accidents (EMAs), but research into the reliability of human factors is still in its infancy in the field of EMAs, and further investment in research is urgently required. To analyze the HOFs in EMAs, a hybrid method including the Human Factors Analysis and Classification System (HFACS) and fuzzy fault tree analysis (FFTA) was applied to EMAs for the first time in the paper. HFACS is used to identify and classify the HOFs with 135 accidents, reorganized as basic events (BEs), intermediate events (IEs), and top event (TE), and develop the architecture of fault tree (FT). Fuzzy aggregation is employed to address experts’ expressions and obtain the failure probabilities of the BEs and the minimal cut sets (MCSs) of the FT. The approach generates BEs failure probabilities without reliance on quantitative historical failure statistics of EMAs via qualitative records processing. The FFTA–HFACS model is applied for quantitative analysis of the probability of failure of electrical mishaps and the interaction between accident risk factors. It can assist professionals in deciding whether and where to take preventive or corrective actions and assist in knowledgeable decision-making around the electric operation and maintenance process. Finally, applying this hybrid method to EMAs, the results show that the probability of an EMAs is 1.0410 × 10−2, which is a risk level that is likely to occur and must be controlled. Two of the most important risk factors are habitual violations and supervisory violation; a combination of risk factors of inadequate work preparation and paralysis, and irresponsibility on the part of employees are also frequent errors.

Importance analysis based on universal grey operation for an aircraft landing gear retraction system

In order to improve the reliability and performance of landing gear retraction systems, this paper presents two importance analysis methods based on universal grey operation. According to the system principle and fault mechanisms, the fault tree of the retraction system was first established. The uncertainties of the bottom events were then described using the universal grey number to obtain the universal grey representation of the system failure probability. And compared with the traditional interval operation, the results show that universal grey operation can solve the problem of interval expansion with uncertainty. Importance analyses of the bottom events were then conducted based on the probability importance and the key importance. By comparing the two important indices of the bottom events, the larger the value is, the higher the importance is. It was found that the occurrence of the bottom events “pipeline oil leakage,” “pump motor damage,” and “oil pollution” had the greatest impact on system failure probability, thus determining the key weak links affecting system failure and indicating the most effective targets for improvement.

Multi-state Risk-Based Maintenance Analysis of Redundant Safety Systems Using the Markov Model and Fault Tree Method

The risk-based maintenance strategy has received special attention in the safe operation of nuclear power plants. Simultaneous quantification of the positive and negative effects of maintenance activities and components degradation effect makes it possible to accurately evaluate the risk criterion for safety systems of nuclear power plants. However, it is difficult to integrate the effects of maintenance and components degradation into the standard reliability approaches. A straightforward approach for considering components degradation and different maintenance policies is to make use of Markov maintenance models. In this article, the effectiveness of maintenance activities (including changes in the surveillance test intervals and alteration in the different maintenance policies) on the components unavailability with considering aging effects is quantified using Markov maintenance models and then by coupling these models and the fault tree method, the risk measure is upgraded from the component level to the system level. The proposed models are applied to evaluate the unavailability of two safety systems of VVER-1000/V446 nuclear power plants as case studies. The results show that the Markov method due to its multi-state nature is effective in the conservative evaluation of risk measures so that the unavailability computed by the coupling process is higher than the original unavailability (calculated by system fault tree using PSA data of nuclear power plants) for all maintenance policies. In addition, this study illustrates that the developed Markov maintenance models could be applied to the large-scale whole plant level and provides a proper transition from the classical PSA methods to new techniques. This approach integrates the effects of maintenance strategies and components degradation. Also, it provides a practical and a more accurate tool to determine the technical specification of a real nuclear power plant from the risk point of view.

Method for probabilistic evaluation of post-earthquake functionality of building systems

Recent large earthquakes have caused significant building damage resulting in lengthy functional recovery times. Post-earthquake recovery time can be minimized through improved design of building systems essential for providing continuous operation. This study presents a novel method for probabilistic evaluation of the post-earthquake building’s functionality to support functionality-based seismic design. Aligned with the PBEE methodology, the proposed method utilizes FEMA P-58 damage assessment results in conjunction with fault trees of complex building systems to find the percent of building area with compromised functionality and to identify building components that impair its functionality. The primary user-defined inputs are probabilistic limit state functions of individual building components, which define the damage thresholds for partial (local) and full loss of the building functionality. The proposed method is highly flexible; it has a modular structure that allows easy extension by adding new features. A case study is conducted for an existing 13-story building to demonstrate the functionality evaluation and showcase how its results can guide the retrofit measures for improved seismic performance.

Reliability analysis on the train control system in the CTCS-3 operating mode

Purpose This paper aims to study the reliability of the high-speed train operation control system in the Chinese Train Control System Level 3 (CTCS-3) operating mode. Design/methodology/approach Dynamic fault tree and Bayesian network method are adopted to analyze the reliability and weakness of the CTCS-3 system. Findings First, a physical architecture and data flow diagram of the CTCS-3 system are established according to the typical structure and functions of the CTCS-3 system. Second, the dynamic fault tree of the CTCS-3 system is constructed. Considering the prior probability of the bottom event and the existence of dynamic redundancy, the dynamic fault tree is transformed into a Bayesian net. The reliability of the CTCS-3 system is carried out based on the prior probability and the weakness that affects the reliability of the system based on the posterior probability is also analyzed by the Bayesian network. Finally, it is disclosed that the impact of the on-board subsystem on the reliability of the CTCS-3 system is generally greater than that of the ground subsystem. The two weakest modules in the onboard subsystem are the driver-machine interface (DMI) and balise transmission module (BTM) and the weakest one in the ground subsystem is Balise. The analysis results are generally consistent with the malfunctions in the field operation of China’s high-speed railway. Originality/value (1) By reasoning, the reliability of the train operation control system in the CTCS-3 operating mode meets the standard requirements. (2) Through backward reasoning, it is found that the failure of the onboard subsystem leads to a greater probability of failure of the train control system. (3) The DMI, BTM and automatic train protection computer unit modules are weak components in the onboard subsystem. Vital digit input&output, train interface unit and train security gateway are rarely involved in previous research, the result in this paper shows that these three modules are also weak components in the subsystem, which requires attention.