Analysis on reliability model of high-speed EMU bogie system

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.

A Bayesian network (BN) Charniak (1991) Pearl (1988) Jensen (1996) Neapolitan (1990) is a directed acyclic graph (DAG) consisting of nodes and arrows, in which node represents random variables, and arrow represents dependence relationship between connected nodes in the sense of the probabilistic, deterministic, or functional. Each node in BN has a specified conditional probability distribution (CPD), where all CPDs together parameterize the model. BNs have been used as powerful probabilistic knowledge models for decision support under uncertainty over a few decades, with numerous applications such as classification, medical diagnosis, bioinformatics, speech recognition, etc. One of the most important features BN has is the factorization of the joint probability space, so that conditional independence can be exploited to simplify modeling and save computations. However, BN model is only useful when combined with efficient algorithms for inference.

Fault tree analysis (FTA) is one of the most prominent techniques used in risk analysis, this method aimed to identify how component failures lead to system failure using logical gates (i.e. AND, OR gates). However, some limitations appear on FTA due to its static structure. Bayesian networks (BNs) have become a popular technique used in reliability analysis; it represents a set of random variables and their conditional dependencies. This paper discusses the advantages of Bayesian networks over fault tree in reliability and safety analysis. Also, it shows the ability of BN to update probabilities, to represent multi-state variables, dependent failures, and common cause failure. An example taken from the literature is used to illustrate the application and compare the results of both fault tree and bayesian networks techniques.

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.

In view of the current study on the reliability analysis for complex new equipment systems, the fault tree analysis method is used in reliability engineering (Fault Tree Analysis, FTA). However, qualitative and quantitative analysis for complex equipment systems reliability are difficult problems. In this paper, firstly, fault tree analysis method is introduced, secondly, through an instance of a certain type of equipment, mathematical description and analysis methods for qualitative and quantitative analysis of system reliability are carried out. Finally, the results from fault tree analysis is presented and discussed, which shows that FTA is a method can quickly and accurately analyze equipment systems reliability, and is a designer tool for evaluation and improvement of system reliability.

One of the most important methods in reliability analysis is Fault Tree Analysis that over time it has been extended into the more versatile method of Dynamic Fault Tree (DFT) Analysis. In most cases, exact evaluation of system reliability using fault tree due to component limited data especially owning to failure rates, is difficult. In this paper, the Fuzzy Time-To-Failure (FTTF) model based on Fuzzy Lower and Upper (L-U) bounds is developed to evaluate the reliability of system and solve aforementioned problems. This process completed by proposed Fuzzy Monte Carlo Simulation (FMCS) throughout the preferred operational time and uses the actual types of fuzzy failure distribution. FMCS is done based on Lower-Upper bounds for each event failure rates. Using fuzzy arithmetic, events FTTF are generated, and then, the Top Event failure curve and the reliability profile of the system are evaluated. The results show that the proposed method not only is feasible and powerful but can also accurate more than the other probabilistic and Possibilistic techniques. Finally, this model is implemented in an Emergency Detection System (EDS) which is a useful system in aerospace and space applications.

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.

This paper proposes a Fault Tree Analysis (FTA) method for control program (CP) for space systems. Once a serious fault occurs in CP, a space system is lost. To avoid such situation, methods that can clarify its causes and propose its countermeasure are required. The concepts of proposed FTA methods are followings: preparing Fault Tree (FT) templates corresponding to primitive statements in CP, defining FTA procedure, and defining CP rewrite method that makes it easier to conduct FTA. The proposed method is helpful to conduct FTA. As a result of applying the proposed FTA method to the fault that a spinning satellite rotates too fast, we confirm that the proposed method can clarify the causes of the fault.

Objective To investigate IMRT planning process using the combined application of failure modes and effects analysis (FMEA) and fault tree analysis (FTA) by reference to the report of Task Group 100 of the AAPM, and stablish and optimize the quality. Methods A multidisciplinary team detailed the process mapping of IMRT planning using Eclipse TPS. The team evaluated the potential failure modes (FMs) of every process step. The evaluation was divided into two groups according to whether quality management (QM) was considered. For every FM, occurrence (O), severity (S) and detectability (D) by consensus were evaluated, and the product of O, S and D yielded the risk priority number (RPN), which permitted the ranking of the FMs. Finally, FTA was used to determine the root factors contributing to the riskiest failure modes. Results The IMRT plan process consisted of 10 major sub-processes and 33 steps, which amounted to 47 failure modes. For the group without quality management, the RPN of FMs was between 13.2-271.8, 27 of which had RPN≥80, and 18 FMs had S≥8. For the group with quality management, the RPN of FMs was between 11.2-158.4, 11 of which had RPN≥80. The difference of RPN between the two groups was statistically significant (RPN of the group without QM=101.17±66.34, RPN of the group with QM=59.54±35.64, t=8.501, P<0.05). Finally, FTA was used to determine the root factors contributing to the FMs, i. e., prescription dose definition and importing images. Conclusions The FMEA and FTA methods are operable and practical, which can systematically and comprehensively analyze the potential failures and risks existing in the process of IMRT plan. And the FMEA and FTA can contribute to establish and optimize the quality management program in radiotherapy. Key words: Intensity modulated radiotherapy; Failure modes and effects analysis; Failure tree analysis; Quality management

Event trees are a popular technique for modelling accidents in system safety analyses. Bayesian networks are a probabilistic modelling technique representing influences between uncertain variables. Although popular in expert systems, Bayesian networks are not used widely for safety. Using a train derailment case study, we show how an event tree can be viewed as a Bayesian network, making it clearer when one event affects a later one. Since this effect needs to be understood to construct an event tree correctly, we argue that the two notations should be used together. We then show how the Bayesian Network enables the factors that influence the outcome of events to be represented explicitly. In the case study, this allowed the train derailment model to be generalised and applied in more circumstances. Although the resulting model is no longer just an event tree, the familiar event tree notation remains useful.

Fault Tree Analysis (FTA) is a hotspot both in academic field and in modern industry. The FTA method is a means that evaluates the system reliability and safeties, using to predict and examine the faults, analyzing the weak link of system, guiding to circulate and maintain, and the optimization design of system. Along with the technical exaltation of the computer science, the computer aided technology has already become an important method to the fault tree analysis. In order to explore a effective FTA model for modern industry domain in China, this paper studied FTA and its development, analyzed the problems existed in FTA as well as the deficiency in implementing FTA, and proposed an integrated FTA system - Computer Aided FTA System (CAFTA). Finally, this paper expounded the process of the system's designing and implementing based on UML and J2EE technology.

Abstract. CV. Cipta Karya Mandiri is a company engaged in the manufacture of electronic device spare parts in the form of toroidal inductors. Based on the results of the study, in 2020 the company experienced a significant increase in demand compared to 2019. However, it turns out that there is a problem at the company, namely an increase in the percentage of toroidal inductor defects. This shows that the company's efforts to reduce product defects are still not optimal. So that the company needs to know the cause of disability as effort to reduce and eliminate defects that occur. The purpose of this study is to identify the causes of defective toroidal inductor products. The method used to reduce or even eliminate toroidal inductor product defects is by using the Fault Tree Analysis (FTA) method. The process of identifying the causes of failure is carried out on five types of defects which are included in 80% of the cumulative defects on the Pareto diagram. As for what is included in the 80% cumulative Pareto diagram defects consist of loose winding defects, bad windings, bonding overs, red pins, and wound wires. The results of the identification of the causes of defects from the five types of defects using fault tree analysis (FTA), namely the factors that cause defects to consist of human or worker factors, machine factors, and tools used during the production process.&#x0D; Abstrak.CV. Cipta Karya Mandiri is a company engaged in the manufacture of electronic device spare parts in the form of toroidal inductors. Based on the results of the study, in 2020 the company experienced a significant increase in demand compared to 2019. However, it turns out that there is a problem at the company, namely an increase in the percentage of toroidal inductor defects. This shows that the company's efforts to reduce product defects are still not optimal. So that the company needs to know the cause of disability as effort to reduce and eliminate defects that occur. The purpose of this study is to identify the causes of defective toroidal inductor products. The method used to reduce or even eliminate toroidal inductor product defects is by using the Fault Tree Analysis (FTA) method. The process of identifying the causes of failure is carried out on five types of defects which are included in 80% of the cumulative defects on the Pareto diagram. As for what is included in the 80% cumulative Pareto diagram defects consist of loose winding defects, bad windings, bonding overs, red pins, and wound wires. The results of the identification of the causes of defects from the five types of defects using fault tree analysis (FTA), namely the factors that cause defects to consist of human or worker factors, machine factors, and tools used during the production process.

The secondary system of the intelligent substation has a complex logic structure and a variety of failure modes, making it difficult and time-consuming to locate the fault. Aiming at the shortcomings of traditional fault location methods, a fault tree-based positioning method is proposed. Fault tree analysis method can effectively react to fault alarms and the causal relationship between fault locations that cause them. The fault tree analysis method is applied to the expert system, which solves the problems of low efficiency, lack of knowledge and inaccurate reasoning results of the traditional rule-based expert system, and the logical relationship between knowledge is not reflected. Aiming at the difficulty of obtaining the probability of the event at the bottom of the fault tree, the fuzzy number method is proposed to improve the traditional fault tree method, and the triangular fuzzy number is used to express the fuzzy probability of the bottom events, thereby improving the efficiency of the fault maintenance and positioning of the maintenance personnel.

This research was conducted at UD. SI in order to control the quality of plastic rack products by applying the lean six sigma method and fault tree analysis. This method is often applied when a problem occurs because it is effective as a data processing method and analyzes a major failure and finds the cause of a defect in the production process in controlling product quality and improving quality in the company. This research uses Lean Six Sigma and Fault Tree Analysis methods. This data retrieval is done by using primary data and secondary data. Primary data was obtained through observation, interviews with employees and staff in the production department, while secondary data was obtained from the company when conducting research. From the results of the study that for perforated shelves, the error rate achieved can be seen in the pareto diagram where there are three types, namely perforated shelves, crinkled shelves and thin shelves where the causative factors of the product can be seen in the fault tree analysis method, namely humans and machines as well as recommendation for improvement by monitoring and checking as well as operators at the time of production as well as conducting strict product quality checks which are expected to minimize repeated repetitions.

This paper deals with the reliability assessment of electrical power supply systems under redundancy. For doing so, three probabilistic safety analysis (PSA) methods are proposed and developed namely fault tree analysis (FTA), reliability block diagram (RBD), and Monte Carlo Simulation (MC) method. The FTA method is performed using RiskSpectrumPSA® software and the RBD and MC methods are coded using Matlab software. The electrical power supply system reliability is analyzed and enhanced considering various cases of redundancy. The FTA method is based on qualitative and quantitative reliability assessment. The quantitative assessment calculates the failure probability of the top event, and the qualitative approach determines the minimal cut sets (MCS). However, the RBD and MC methods are proposed to compute the system failure probability. The considered methods are applied on three case studies and the obtained results are compared to each other. The obtained results show that the three methods provide almost the same results. Furthermore, the reliability of the electrical power supply is clearly enhanced with the number of redundant components.