Design of reliable systems using static and dynamic fault trees

A fixture plays a key role in ensuring proper metal fit-up in sheet metal assembly with laser welding. In this paper, an optimal fixture configuration design model is proposed based on a new locating scheme which includes both total locating and direct locating on welds. In this model, both the number and the location of the concerned locators are taken as objectives. The proposed prediction and correction method is used for determining the number of direct locators, and a pattern sorting method is developed for determining the number of total locators. Considering the degree of metal fit-up (DMF) to be a fuzzy quantity, a feasible evaluation criterion of DMF is also developed using a fuzzy synthesis evaluation method. A powerful optimisation technique - genetic algorithm - is employed as the optimisation procedure. A case study is presented which demonstrates the effectiveness of the optimal model, and the degree of metal fit-up can be improved compared to the initial locating scheme.

Nowadays, several components and systems are designed and produced based on reliability. Since the reliability criterion has an important role in purchasing and implementation of these systems. In the design of a reliable system, fault and failure analysis must be carried out in order to reduce fault probability of the system. When dependency and the relation between components of a complex system are important and should be mentioned, determination of system reliability is very difficult. In this paper, dynamic fault tree is used to evaluate the systems reliability that their behavior is varied with time. Dynamic fault tree is constructed and then it converted to dynamic bayesian network. In this paper, the principle of dynamic fault tree gates and their mapping into dynamic bayesian are explained and some new relations between events and gates for this mapping are proposed. GeNIe package is used to determine dynamic bayesian network based on stochastic sampling algorithms. Four systems (cardiac assist system, hypothetical cascaded priority-and system, inertial navigation system/ global positioning system integrated, and emergency detection system) are investigated; reliability and fault probability of these systems are calculated. Comparison of the results with those obtained by other researches shows the proposed method effectiveness for systems reliability modeling and assessment via dynamic bayesian network.

Background: This article reviews recent patents pertaining to the technological achievements and shortcomings of aircraft Position Indicator and Actuator System (PIAS). Generally, most patents have concentrated more on design, while implementing various developed single or multiobjective algorithms to solve component or system optimal design problems such as those of PIAS. Thus, there is a noticeable lack of patent efforts on either developing new constraint-handling techniques especially for multi-objective optimization, or applying advance single-objective constrainthandling techniques to multi-objective optimization. Method: To address this, the authors of this article in there past work developed a multi-objective optimization algorithm called Genetic Pareto Set Identification Algorithm plus Different Sex (GPSIA+DS), which considers the feasible and infeasible solution in it optimization strategy. However, the application of GPSIA+DS to real world reliability engineering problem has not been validated. Result: Thus, in this work, GPSIA+DS ability to handle an aircraft PIAS optimization problem was presented. PIAS component failure rates were obtained via Fault Tree Analysis (FTA). PIAS cost and reliability were the study objective functions subject to weight constrains. The performance of GPSIA+DS for the test problem was investigated through a comparative study. Conclusion: In the comparative study GPSIA+DS was compared with fast-sort genetic algorithm plus constrained dominance principle (NSGA-II+CD). Although NSGA-II+DS out performed GPSIA+DS for this test problem, it is evident that GPSIA+DS is equally an efficient algorithm. Keywords: Aircraft system, constrain, failure rate, genetic algorithm, multi-objective optimization (MOO), pareto set, reliabilityAircraft system, constrain, failure rate, genetic algorithm, multi-objective optimization (MOO), pareto set, reliability

Dynamic Fault Trees (DFT) [15] [11] extend the classic fault tree methodology by allowing the modelling of complex system and component's behaviors and interactions. DFTs are experiencing a growing success among reliability engineers and therefore, the possible advantages of the DFTs were investigated using the case studies defined by NRG. This paper presents one of the case studies used for the investigation of DFT's advantages over the existing static fault tree methodologies. The case study presented in this paper assesses the influence of spare parts data on a Fault Tree (FT) model.

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.

The unreliability evaluation of a system including dependencies involving the state of components or the failure events, can be performed by modelling the system as a dynamic fault tree (DFT). The combinatorial technique used to solve standard Fault Trees is not suitable for the analysis of a DFT. The conversion into a dynamic Bayesian network (DBN) is a way to analyze a DFT. This paper presents a software tool allowing the automatic analysis of a DFT exploiting its conversion to a DBN. First, the architecture of the tool is described, together with the rules implemented in the tool, to convert dynamic gates in DBNs. Then, the tool is tested on a case of system: its DFT model and the corresponding DBN are provided and analyzed by means of the tool. The obtained unreliability results are compared with those returned by other tools, in order to verify their correctness. Moreover, the use of DBNs allows to compute further results on the model, such as diagnostic and sensitivity indices

Dynamic Fault Trees (DFT) extend standard fault trees by allowing the modeling of complex system components’ behaviors and interactions. Being a high level model and easy to use, DFT are experiencing a growing success among reliability engineers. Unfortunately, a number of issues still remains when using DFT. Briefly, these issues are (1) a lack of formality (syntax and semantics), (2) limitations in modular analysis and thus vulnerability to the state-space explosion problem, and (3) lack in modular model-building. We use the input/output interactive Markov chain (I/O-IMC) formalism to analyse DFT. I/O-IMC have a precise semantics and are an extension of continuous-time Markov chains with input and output actions. In this paper, using the I/O-IMC framework, we address and resolve issues (2) and (3) mentioned above. We also show, through some examples, how one can readily extend the DFT modeling capabilities using the I/O-IMC framework.

Considering the dynamic characteristics of Integrated Modular Avionics (IMA) core processing system, such as fault-tolerant redundancy and sequence correlation, it is not enough to simply determine the results of different combinations of fault events, it is also necessary to understand the correlation of their failures. The Dynamic Fault Tree (DFT) analysis method is applied to the safety design of the IMA core processing system to ensure that the system design meets the safety requirements of aircraft. However, Dynamic Fault Tree Analysis (DFTA) requires a lot of manual effort, it will cause the state space explosion and low calculation accuracy, which makes it time-consuming and expensive. This paper presents a dynamic fault tree analysis based on Discrete-Time Bayesian Network (DTBN). Aimed at verifying the characteristics of the presented approach, we conduct a comparative experiment on the accuracy of calculating top events probability under DTBN method and Markov chain analysis method. Finally, we draw the conclusion that the DTBN method has higher accuracy and can effectively avoid space-and-state explosion at the same time.

Fault trees constitute one of the essential formalisms for static safety analysis of various industrial systems. Dynamic fault trees (DFT) enrich the formalism by support for time-dependent behaviour, e.g., repairs or dynamic dependencies. This enables more realistic and more precise modelling, and can thereby avoid overly pessimistic analysis results. But analysis of DFT is so far limited to substantially smaller models than those required for instance in the domain of nuclear power safety. This paper considers so called SD fault trees, where the user is free to express each equipment failure either statically, without modelling temporal information, or dynamically, allowing repairs and other timed interdependencies. We introduce an analysis algorithm for an important subclass of SD fault trees. The algorithm employs automatic abstraction techniques effectively, and thereby scales similarly to static analysis algorithms, albeit allowing for a more realistic modelling and analysis. We demonstrate the applicability of the method by an experimental evaluation on fault trees of nuclear power plants.

Criteria for evaluating protection from single points of failure for partially expanded fault trees

Fault trees are a key technique in safety and reliability engineering. Their application includes aerospace, nuclear power, car, and process engineering industries. Various fault tree extensions exist that increase expressiveness while yielding succinct models. Their analysis is a main bottleneck: techniques do not scale and require manual effort. Formal methods have an enormous potential to solve these issues. We discuss a mixture of formal method techniques resulting in a fully automated and scalable approach to analyze Dugan’s dynamic fault trees.

Switched-transition method is being implemented in aero network. The commercial standard ARINC664 and the specification in aircraft implementation named Avionics Full Duplex Switched Ethernet (AFDX) have been formed to describe the topology and the use of switched ethernet in avionics application. Reliability verification of the whole architecture and work scheme should be conducted before implemented in safty-critical system. In this paper, different types of causes of frame-e2e-failure are synthesized in fault tree, in which function losses of topological nodes, such as swithes, end-systems and links, are conceptually constructed in accordance with its practice network counterpart. While the topological node is functional, frame-loss derived from specified mechanism and redundency algorithm in AFDX are also constructed in a dynamic fault tree. The synthesis fault tree are combined and computed by Galileo Tool according failure distribution information of each basic event.

Several advances in fault tree models have been developed as an aid in the analysis of computer systems. Such advances include the ability to model sequence dependencies, the application of fault trees to the analysis of hardware and software fault tolerant systems, the ability to include details of the recovery process in the fault tree solution, and the use of modularization and binary decision diagrams as an aid in solution. In this paper we present a software package (DIFtree) that implements these advances into a single methodology that can solve both dynamic and static fault trees, and which is applicable to the analysis of hardware, software and humanware in complex computer-based systems.

An extended HAZOP analysis approach with dynamic fault tree

Complex sociotechnical systems with multiple competing objectives and nonlinear dynamics pose significant challenges for policy optimization. Traditional simulation-based methods often struggle with high-dimensional policy spaces. This study addresses these challenges by combining genetic algorithms (GAs) with system dynamics (SD) modeling to optimize policy configurations in complex environments. Our approach merges SD’s capacity to simulate intricate system behaviors with GA’s prowess in multi-objective optimization. The SD model uses predefined decision variables to simulate system behavior, while GA iteratively adjusts these variables to find optimal policy configurations. We apply this hybrid approach to a media industry case study, focusing on balancing profit, competitiveness, and audience satisfaction. The research methodology integrates SD’s ability to capture complex system behaviors with GA’s strength in optimizing multiple objectives. An SD model simulates system behavior based on predefined decision variables representing key policy levers. GA then iteratively adjusts these variables based on fitness objectives derived from the SD model. This process evaluates performance and identifies the globally optimal policy configurations. The results show that the hybrid SD–GA framework significantly improves policy solutions compared to conventional methods. Sensitivity analysis confirms the optimized policies’ robustness and comparative assessments highlight our approach’s advantages in navigating complex policy spaces. This study introduces a new SD–GA framework that improves data-driven policy formulation in complex systems. It combines policy informatics and evolutionary algorithms to offer a comprehensive approach to multi-criteria decision-making. Future research could include long-term validation of this approach in the broadcasting industry to further refine and apply this methodology across various domains.