Search results

This paper aims to present a method for calculating the top event probability of a fault tree with priority AND gates.

The paper makes use of Merle's temporal operators for obtaining the minimal cut sequence set of a dynamic fault tree. Although Merle's expression is based on the occurrence time of an event sequence, the paper treats the expression as an event containing the order of events. This enables the authors to treat the minimal cut sequence set by using the static fault tree techniques. The proposed method is based on the sum of disjoint products. The method for a static FT is extended to a more applicable one that can deal with the order operators proposed by Merle et al.

First, an algorithm to obtain the minimal cut sequence set of dynamic fault trees is proposed. This algorithm enables the authors to analyze reasonably large scale dynamic fault trees. Second, the proposed method of obtaining the top event probability of a dynamic fault tree is efficient compared with an inclusion‐exclusion based method proposed by Merle et al. and a conventional Markov chain approach. Furthermore, the paper shows the top event probability is derived easily when all the basic events have exponential failure rates.

The methodology presented shows a new solution for calculating the top event probability of dynamic fault trees.

This paper aims to deal with the problems such as epistemic uncertainty, common cause failure (CCF) and dynamic fault behaviours that arise in complex systems and develop an effective fault diagnosis method to rapidly locate the fault when these systems fail.

First, a dynamic fault tree model is established to capture the dynamic failure behaviours and linguistic term sets are used to obtain the failure rate of components in complex systems to deal with the epistemic uncertainty. Second, a β factor model is used to construct a dynamic evidence network model to handle CCF and some parameters obtained by reliability analysis are used to build the fault diagnosis decision table. Finally, an improved Vlsekriterijumska Optimizacija I Kompromisno Resenje algorithm is developed to obtain the optimal diagnosis sequence, which can locate the fault quickly, reduce the maintenance cost and improve the diagnosis efficiency.

In this paper, a new optimal fault diagnosis strategy of complex systems considering CCF under epistemic uncertainty is presented based on reliability analysis. Dynamic evidence network is easy to carry out the quantitative analysis of dynamic fault tree. The proposed diagnosis algorithm can determine the optimal fault diagnosis sequence of complex systems and prove that CCF should not be ignored in fault diagnosis.

The proposed method combines the reliability theory with multiple attribute decision-making methods to improve the diagnosis efficiency.

To 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.

In 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.

The 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.

The research results of this paper can be used as supporting evidence for airworthiness compliance of airborne inerting system.

The research results of this paper can provide practical guidance for the current civil airworthiness certification work.

The purpose of this paper is to facilitate perishable product supply chain (PPSC) managers and practitioners to assess PPSC failure events. The paper proposed fault tree methodology for assessing failures associated with PPSC for evaluating the performance in terms of effective PPSC management adoption.

Initially, different failure events were identified from literature and semi-structured interviews from experts. Fault tree model was developed from the identified failure events. Probability of failure events was calculated using Poisson distribution based on the annual reports and interviews conducted from experts. Further, qualitative analysis – minimum cut sets (MCSs), structural importance coefficient (SIC) – and quantitative analysis – Birnbaum importance measure (BIM), criticality importance factor (CIF) and diagnosis importance factor (DIF) – were performed for ranking of failure events. In this study, fault tree development and analysis were conducted on apple supply chain to present the authenticity of this method for failure analysis.

The findings indicate that the failure events, given as failure at production and procurement (A2), that is, involvement of middleman (BE3), handling and packaging failure (BE4) and transportation failure (A3), hold the highest-ranking scores in analysis of PPSC using fault tree approach.

This research uses the modularization approach for evaluation of failure events of PPSC. This paper explores failures related to PPSC for efficient management initiatives in apple supply chain context. The paper also provides suggestion from managerial perspective with respect to each failure event.

This paper aims to deal with the problems of failure dependence and common cause failure (CCF) that arise in reliability analysis of complex systems.

Firstly, a dynamic fault tree (DFT) is used to capture the dynamic failure behaviours and converted into an equivalent generalized stochastic petri net (GSPN) for quantitative analysis. Secondly, an efficient decomposition and aggregation (EDA) theory is combined with GSPN to deal with the CCF problem, which exists in redundant systems. Finally, Birnbaum importance measure (BIM) is calculated based on the EDA approach and GSPN model, and it is used to take decisions for system improvement and fault diagnosis.

In this paper, a new reliability evaluation method for dynamic systems subject to CCF is presented based on the DFT analysis and the GSPN model. The GSPN model is easy to capture dynamic failure behaviours of complex systems, and the movement of tokens in the GSPN model represent the changes in the state of the systems. The proposed method takes advantage of the GSPN model and incorporates the EDA method into the GSPN, which simplifies the reliability analysis process. Meanwhile, simulation results under different conditions show that CCF has made a considerable impact on reliability analysis for complex systems, which indicates that the CCF should not be ignored in reliability analysis.

The proposed method combines the EDA theory with the GSPN model to improve the efficiency of the reliability analysis.

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.

Dynamic fault tree and Bayesian network method are adopted to analyze the reliability and weakness of the CTCS-3 system.

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.

(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.

This study aims to solve the problem that the traditional hierarchically performed hazard origin and propagation studies (HiP-HOPS) cannot make dynamic model for the complex system such as integrated modular avionics (IMA) system.

A new combination method that combines HiP-HOPS with architecture analysis and design language (AADL) is proposed.

The combination method potentially reduces the amount of rework required for safety analysis and modelling of a modified design.

Modelling the IMA system with the combination method can just make qualitative analysis but cannot make quantitative analysis.

The static model depicts the fault propagation among the components while the dynamic model describes the composite fault with AADL for IMA system.

The results of the case study show that the proposed method not only keeps model consistency but also makes safety analysis and modelling for IMA system efficiently.

Delays in construction projects are both disruptive and expensive. Thus, potential causes of schedule deviation need to be identified and mitigated. In previous research, delay factors were predominantly identified through surveys administered to stakeholders in construction projects. Such delay factors are typically considered individually and presented at the same level without explicitly examining their sequence of occurrence and inter-relationships. In reality, owing to the complex structure of construction projects and long execution time, non-conformance to schedule occurs by a chain of cascading events. An understanding of these linkages is important not only for minimising the delays but also for revealing the liability of stakeholders. To explicitly illustrate the cause–effect and logical relationship between delay factors and further identify the primary factors which possess the highest significance toward the overall project schedule delay, the fault tree analysis (FTA) method, a widely implemented approach to root cause problems in safety-critical systems, has been systematically and rigorously executed.

Using a case study, the in-depth analysis for identifying the most fundamental delay factors has been fulfilled through FTA's tree structure. The logical deduction for mapping and visualising the chronological and cause–effect relationships between various delay factors has been conducted through the logical gate functions of FTA based on the data collected from the site event log, pre-fabricated structural component manufacturing log and face-to-face interview with project stakeholders.

The analysis identified multiple delay factors and showed how they are linked logically and chronologically from the primary causes to the ultimate undesired event in a rigorous manner. A comparison was performed between the proposed FTA model and the conventional investigation method for revealing the responsibility employed in the construction industry, consisting of event logs and problem reports. The results indicate that the FTA model provides richer information and a clearer picture of the network of delay factors. Importantly, the ability of FTA in revealing the causal connection between the events leading to the undesired delays and in comprehending their prominence in the real-world construction project has been clearly displayed.

This study demonstrates a new application of FTA in the construction sector allowing the delay factors to be understood and visualised from a new perspective. The new approach has practical use in finding and removing root causes of the delay, as well as clarifying the attribution of responsibility that causes the delay.

This paper aims to present two methods for calculating the steady state probability of a repairable fault tree with priority AND gates and repeated basic events when the minimal cut sets are given.

The authors consider a situation that the occurrence of an operational demand and its disappearance occur alternately. We assume that both the occurrence and the restoration of the basic event are statistically independent and exponentially distributed. Here, restoration means the disappearance of the occurring event as a result of a restoration action. First, we obtain the steady state probability of an output event of a single‐priority AND gate by Markov analysis. Then, we propose two methods of obtaining the top event probability based on an Inclusion‐Exclusion method and by considering the sum of disjoint probabilities.

The closed form expression of steady state probability of a priority AND gate is derived. The proposed methods for obtaining the top event probability are compared numerically with conventional Markov analysis and Monte Carlo simulation to verify the effectiveness. The result shows the effectiveness of the authors’ methods.

The methodology presented shows a new solution for calculating the top event probability of repairable dynamic fault trees.

This study is a systematic literature review of the application of artificial intelligence (AI) in safety-critical systems. The authors aim to present the current application status according to different AI techniques and propose some research directions and insights to promote its wider application.

A total of 92 articles were selected for this review through a systematic literature review along with a thematic analysis.

The literature is divided into three themes: interpretable method, explain model behavior and reinforcement of safe learning. Among AI techniques, the most widely used are Bayesian networks (BNs) and deep neural networks. In addition, given the huge potential in this field, four future research directions were also proposed.

This study is of vital interest to industry practitioners and regulators in safety-critical domain, as it provided a clear picture of the current status and pointed out that some AI techniques have great application potential. For those that are inherently appropriate for use in safety-critical systems, regulators can conduct in-depth studies to validate and encourage their use in the industry.

This is the first review of the application of AI in safety-critical systems in the literature. It marks the first step toward advancing AI in safety-critical domain. The paper has potential values to promote the use of the term “safety-critical” and to improve the phenomenon of literature fragmentation.