A new method for fuzzy fault tree analysis

α-Cut method based importance measure for criticality analysis in fuzzy probability – Based fault tree analysis

Fault tree analysis is regarded as a powerful technique for assessing system reliability. The conventional approach of solving fault trees is based on applying probability theory by assigning probability distributions to basic events and propagating the probabilities to calculate the overall probability of the top event. However, applying this technique, especially in construction projects, entails several difficulties attributed to the lack of sufficient data to estimate the probability distributions for basic events. Thus, risk analysts tend to assume probability distributions of basic events, which adds further uncertainty to the analysis. To overcome these difficulties, this paper presents a simplified approach in which basic events are represented by a possibility distribution, and the possibility distribution of the top event probability is calculated by applying fuzzy arithmetic operations. The method presented herein is demonstrated using a case study to quantify the probability of failure of horizontal directional drilling (HDD). The proposed approach is able to overcome the difficulties of using the conventional approach by relying on linguistic terms rather than assuming probability distributions for basic events.

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.

There are many available techniques which are widely used for failure probability analysis. Fault tree analysis (FTA) is a well-known method to identify the basic events (BEs) to reach top event. However, the FTA method in real circumstances is limited because of the many unknown and the vagueness of the situations. Thus, fuzzy set theory with respect to subjective expert opinion is employed to cope with the uncertain knowledge of BEs including randomness, ignorance, and shortages of data. In addition, to gain this purpose, much subjectivity may appear; as an example, the main one is the expert weighting. This study highlights the utility of fuzzy set theory and analytic hierarchy process to failure probability analysis in a case study. A chemical process plant has been selected to illustrate the application of proposed model with a comparison of the results with conventional model.

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.

Fuzzy fault tree analysis for coal burst occurrence probability in underground coal mining

Fuzzy probability on reliability study of nuclear power plant probabilistic safety assessment: A review

Detailed software analysis requires a manageable and exhaustive problem solving technique. An approach, the fault locator and weighting system (FLAWS), is described. FLAWS is coupled with a software tool, the data structure analysis system (DSAS), to facilitate techniques for detailed software analysis. DSAS reads the target system code from source files and builds an exhaustive database of the target code, making detailed analysis of large systems manageable. The commands of a target language have been translated to a compatible graphical representation. Safety critical portions of a 200 KSLOC program have been diagrammed using these graphical elements. The resulting diagrams are schematics of the system logic. These schematics tie all identified inputs to their associated outputs, including all coding errors. The FLAWS process and database permit exhaustive connectivity analysis of new target system code. FLAWS uses conventional fault tree analysis (FTA) symbolism and is generally applicable to large software systems. >

Smart transmitters are increasingly being deployed in safety critical systems. Although the processing and diagnostic features provided by digital systems are advantageous, it introduces additional complexity in reliability modelling of systems. The conventional Fault Tree Analysis (FTA) method for system reliability analysis has its shortcomings in modelling digital systems, to incorporate its dynamic system interaction and failure modes in detail. This is because some of the failure modes may arise due to the dynamic interactions between the modules of the system. Dynamic Flowgraph Methodology (DFM) was identified as one of the optimistic approaches to handle these interactions. DFM approach combines multi‐valued logic modelling and analysis capabilities to handle systems consisting of components that have multiple degraded states and exhibit dynamic behaviour. However, only few DFM tools have been developed and some of them are not available in public domain. The DFM approach relies heavily on development and implementation of its decision tables. The logic information present within the decision tables can be meticulously implemented using Petri Nets. Also, there are plenty of free Petri Nets tools available online. Thus, the idea of DFM was used in this work to capture the dynamic interactions by developing decision tables. The decision tables were transformed into the Petri Net models to perform the analysis. The paper demonstrates the potential of the hybrid DFM – Petri Net approach for dynamic reliability analysis of smart transmitter.

The traditional Fault Tree Analysis (FTA) has limitations in its capacity to process imprecise, incomplete, and subjective failure data that is usually found in railway door safety systems. To overcome this weakness, this paper proposed a new Fuzzy Fault Tree Analysis (FFTA) model based on Interval Type-2 Fuzzy Sets (IT2FS). A two-parameter Weibull distribution is used to model failure probabilities of basic events in a realistic way for the representation of time-dependent failure behaviour based on expert elicitation and maintenance data. Fuzzy logic gates are used to construct the fault tree, and fuzzy probability propagation is carried out through α-cut decomposition, which is backed by a convexity theorem that guarantees valid interval behaviour in the decomposition. To prioritize critical events, an Adapted Wasserstein Distance (AWD) based Fuzzy Importance Index (FII) is proposed, along with a convergence theorem proving the stability of fuzzy failure estimates with the increase in expert information. The actionable numerical values are derived through the Weighted Divided Search Enhanced Karnik-Mendel (WDEKM) algorithm to transform system-level fuzzy risk outputs into actionable numerical values. The framework is implemented in Python 3.11, using standard scientific libraries. Findings indicate the proposed method’s high performance with a Fuzzy Priority Index of 0.0214, Fuzzy Criticality Index of 0.0186, and better sensitivity values (SI = 0.69, NSC = 0.63, RCR = 16.8) than the existing models. These results prove that the proposed framework provides more informative uncertainty-aware risk estimates and sensitivity indicators for safety and maintenance prioritization in railway door systems.

The serious consequences of wildfire urges scholars to pay more attention to control the occurrence of wildfire. Wildfire occurring is considered to be the result of satisfying comprehensive conditions including sufficient forest fuel, high forest fire-danger weather ratings and ignition source in this paper. In order to further study the causes and potential risks of wildfires. An assessment algorithm used for wildfires occurring risk is proposed by combining trapezoidal fuzzy theory and FTA. Furthermore, Xiangxi was chosen as application example, and the result shows that the top five dangerous BEs are ‘High average temperatures (X1)’, ‘illegal sacrifices (burning) (X23)’, ‘large underground humics (X7)’, ‘large weeds and dwarf shrubs (X11)’, ‘luxuriant tree branches (X14)’ exist highly risk. Finally, based on the evaluation results, suggestions are provided for the above basic events with greater risks.

Recently, a new fuzzy fault tree analysis (FFTA) has been developed to propagate and quantify the epistemic uncertainties occurring in qualitative data such as expert opinions or judgments. It is well known that the weakest triangular norm (Tw) based fuzzy arithmetic operations preserve the shape of the fuzzy numbers, provide more exact fuzzy results and effectively reduce uncertainty range. The objective of this paper is to develop a novel Tw-based fuzzy importance measure to identify the critical basic events in FFTA. The proposed approach has been demonstrated by applying it to a case study to identify the critical components of the Group 1 of the U.S. Combustion Engineering Reactor Protection System (CERPS). The obtained results are then compared to the results computed by the existing well-known importance measures of conventional as well as FFTA. The computed results confirm that the proposed Tw -based importance measure is feasible to identify the critical basic events in FFTA in more exact way.

Limitations of conventional fault tree analysis (FTA) application to modern consumer electronics are outlined, and the necessity for an upgraded FTA is justified. Field and manufacturing failure modes, mechanisms and their interactions are arranged into a unified rank system. An advanced failure tree is developed; new application specific gates are substantiated and introduced. Detailed analysis, formulas, typical examples and recommendations are presented. It is shown that degree/accuracy of mathematical description is determined based on demands of practical accuracy. Broader application of the methodology is justified.

Uncertainty in fault tree analysis: A fuzzy approach

PurposeIn recent years, the application of robots in different industrial sectors such as nuclear power generation, construction, automobile, firefighting and medicine, etc. is increasing day by day. In large industrial plants generally humans and robots work together to accomplish several tasks and lead to the problem of safety and reliability because any malfunction event of robots may cause human injury or even death. To access the reliability of a robot, sufficient amount of failure data is required which is sometimes very difficult to collect due to rare events of any robot failures. Also, different types of their failure pattern increase the difficulty which finally leads to the problem of uncertainty. To overcome these difficulties, this paper presents a case study by assessing fuzzy fault tree analysis (FFTA) to control robot-related accidents to provide safe working environment to human beings in any industrial plant.Design/methodology/approachPresented FFTA method uses different fuzzy membership functions to quantify different uncertainty factors and applies alpha-cut coupled weakest t-norm () based approximate fuzzy arithmetic operations to obtain fuzzy failure probability of robot-human interaction fault event which is the main contribution of the paper.FindingsThe result obtained from presented FFTA method is compared with other listing approaches. Critical basic events are also ranked using V-index for making suitable action plan to control robot-related accidents. Study indicates that the presented FFTA is a good alternative method to analyze fault in robot-human interaction for providing safe working environment in an industrial plant.Originality/valueExisting fuzzy reliability assessment techniques designed for robots mainly use triangular fuzzy numbers (TFNs), triangle vague sets (TVS) or triangle intuitionistic fuzzy sets (IFS) to quantify data uncertainty. Present study overcomes this shortcoming and generalizes the idea of fuzzy reliability assessment for robots by adopting different IFS to control robot-related accidents to provide safe working environment to human. This is the main contribution of the paper.