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Probabilistic modeling is widespread in engineering practices, mainly to evaluate the safety, risk analysis, and reliability of complex systems. However, insufficient data makes it difficult to estimate the state probability of components or the global system in dynamic complex systems. Furthermore, conventional methods for dependability analysis typically have little capacity to cope with dependence, failure behavior, epistemic uncertainty, and common cause failure simultaneously. This paper presents the application of an extended discrete-time dynamic evidential network (DEN) model to assess the availability of complex systems. The model application combines Dempster-Shafer's theory to treat epistemic uncertainty over a new state-space reconstruction of components and the dynamic Bayesian network to present multi-state system dependability. This model is demonstrated in a real case study of a water deluge system installed as a safety barrier from Algeria's oil and gas plant. The results show the significant influence of these factors on the system's availability. The goal of this modeling is to assure the high availability of a safety barrier in a volatile setting by providing a decision-making tool to prioritize maintenance tasks, preventing the failure of complicated redundant systems, and recommending alterations to the design.

Continuous emission monitoring system (CEMS) has been widely used in many engineering fields. Common cause failures (CCFs) have remarkable effects on the system reliability of CEMS, because of shared work conditions and dependent failures for different components. A method for reliability evaluation of CEMS with CCFs is proposed based on fuzzy Failure Mode Effects and Criticality Analysis (FMECA) as well as Bayesian network (BN). By introducing the system composition and function principles of CEMS, the CEMS failure mode is clearly defined and the weak components of the system are identified. According to the hazard ranking of the CEMS failure modes, the places where reliability improvement or preventive maintenance should be implemented are found out. Then, BN-based reliability model of the sampling system, which is the weakest subsystem of CEMS, is constructed according to the results of a fault tree analysis. The behavior of CCF is further incorporated, and the α-factor model is used to evaluate the probability of CCF. Lastly, a numerical example is used to illustrate the proposed method. A comparison between the proposed method and the one without considering CCF is carried out. The result demonstrates that the proposed method has better reliability assessment accuracy for the CEMS with CCF than the one without considering CCF.

This work investigates a dynamic solution of human–machine systems with human error and common-cause failure. By means of functional analysis, it is proved that the semigroup generated by the underlying operator converges exponentially to a projection operator by analyzing the spectral property of the underlying operator, and the asymptotic expressions of the system’s time-dependent solutions are presented. We also provide numerical examples to illustrate the effects of different parameters on the system and the theoretical analysis’s validity.

Purpose The internet of things and just-in-time are the embryonic model of innovation for the state-of-the-art design of the service system. This paper aims to develop a fault-tolerant machining system with active and standby redundancy. The availability of the fault-tolerant redundant repairable system is a key concern in the successful deployment of the service system. Design/methodology/approach In this paper, the authors cogitate a fault-tolerant redundant repairable system of finite working units along with warm standby unit provisioning. Working unit and standby unit are susceptible to random failures, which interrupt the quality-of-service. The system is also prone to common cause failure, which tends its catastrophe. The instantaneous repair of failed unit guarantees the increase in the availability of the unit/system. The time-to-repair by the single service facility for the failed unit follows the arbitrary distribution. For increasing the practicability of the studied model, the authors have also incorporated real-time machining practices such as imperfect coverage of the failure of units, switching failure of standby unit, common cause failure, reboot delay, switch over delay, etc. Findings For deriving the explicit expression for steady-state probabilities of the system, the authors use a supplementary variable technique for which the only required input is the Laplace–Stieltjes transform (LST) of the repair time distribution. Research limitations/implications For complex and multi-parameters distribution of repair time, derivation of performance measures is not possible. The authors prefer numerical simulation because of its importance in the application for real-time uses. Practical implications The stepwise recursive procedure, illustrative examples, and numerical results have been presented for the diverse category of repair time distribution: exponential (M), n-stage Erlang (Ern), deterministic (D), uniform (U(a,b)), n-stage generalized Erlang (GE[n]) and hyperexponential (HE[n]). Social implications Concluding remarks and future scopes have also been included. The studied fault-tolerant redundant repairable system is suitable for reliability analysis of a computer system, communication system, manufacturing system, software reliability, service system, etc. Originality/value As per the survey in literature, no previous published paper is presented with so wide range of repair time distribution in the machine repair problem. This paper is valuable for system design for reliability analysis of the fault-tolerant redundant repairable.

Purpose This paper aims to deal with the problems of failure dependence and common cause failure (CCF) that arise in reliability analysis of complex systems. Design/methodology/approach 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. Findings 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. Originality/value The proposed method combines the EDA theory with the GSPN model to improve the efficiency of the reliability analysis.

Corrosion has made petrochemical infrastructure becomes a significant hazard of the surrounding environment. It is an excellent approach to reduce the risk of environmental pollution by improving the accuracy of determining the leakage probability of the equipment system. In this work, a reliability-based methodology was proposed to assess the system leakage probability with multiple pipe segments under the common cause failure (CCF). Specifically, the existence of FD between the pipe segments was examined under the CCF so that a system leakage assessment model was developed considering the FD. Thereby a novelty corrosion-induced leakage risk management framework was developed. After leakage evaluation for an oil pipeline, it was found that the results obtained were too conservative if ignoring FD, which will develop a wasteful maintenance plan. Overall, the findings in this work may be an opportunity for managers to improve maintenance efficiency.

Mission-critical IEC 61850 system architectures are designed to tolerate hardware failures to achieve the highest reliability performance. Hence, multi-channel systems are used in such systems within industrial facilities to isolate machinery when there are process abnormalities. Inevitably, multi-channel systems introduce Common Cause Failure (CCF) since the subsystems can rarely be independent. This paper integrates CCF into the Markov reliability model to enhance the model flexibility to investigate synchronous generator intra-bay SCN architecture reliability performance considering the quality of repairs and CCF. The Markov process enables integration of the impact of CCF factors on system performance. The case study results indicate that CCF, coupled with imperfect repairs, significantly reduce system reliability performance. High sensitivity is observed at low levels of CCF, whereas the highest level of impact occurs when the system diagnostic coverage is 99% based on ISO 13849-1, and reduces as the diagnostic coverage level reduces. Therefore, it is concluded that the severity of CCF depends more on system diagnostic coverage level than the repair efficiency, although both factors impact the system overall performance. Hence, CCF should be considered in determining the reliability performance of mission-critical communication networks in power distribution centres.

Without excellent system uptime and profit margins, many manufacturing systems will not be able to continue operating. Strong business performance of manufacturing companies is facilitated by system availability and profit generation. Most manufacturing systems are set up in series–parallel, parallel-series, or hybrid configurations. In this present study, we analyze a series–parallel system composed of two subsystems with the following specifications: subsystem A consists of two similar units/components that are operated by Human, whereas subsystem B is made up of two similar units/components that are operated by Robot. We have also introduced fault tolerance factor in this work, so that the failure of each unit in each subsystem, common cause failure as well as failure due to human error and robot, will be accompanied by this fault tolerance factor. Our key goals are to examine how fault tolerance will increase the model’s availability and profitability and to identify optimum maintenance plan. In order to meet these key goals, certain expressions for reliability metrics have been developed and validated through numerical examples. Tables and graphs are used to illustrate the results and form conclusions from them.

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value The proposed method combines the reliability theory with multiple attribute decision-making methods to improve the diagnosis efficiency.

Multi-state components, common cause failures (CCFs) and data uncertainty are the general problems for reliability analysis of complex engineering systems. In this paper, a method incorporating fuzzy probability and Bayesian network (BN) into multi-state systems (MSSs) with CCFs is proposed. In particular, basic theories of multi-state BN and fuzzy probability are developed. Moreover, a model integrating CCFs with BN has also been illustrated. In order to incorporate fuzzy probability into MSSs reliability evaluation considering common parent node generated by CCFs, fuzzy probability has to be translated into accurate probability through defuzzification and normalization methods which are both elaborated. In addition, quantitative analysis based on BN is carried out. In this paper, feed system of boring spindle in computer numerical control machine is analyzed as an example to validate the feasibility of the proposed method. It can improve the ability of BN on reliability evaluation of complex system with uncertainty issues.