Optimal fault diagnosis strategy for complex systems considering common cause failure under epistemic uncertainty

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rongxing Duan ◽  
Shujuan Huang ◽  
Jiejun He
Keyword(s):  
Fault Diagnosis ◽  
Complex Systems ◽  
Reliability Analysis ◽  
Epistemic Uncertainty ◽  
Fault Tree ◽  
Common Cause ◽  
Content Type ◽  
Common Cause Failure ◽  
Dynamic Fault Tree ◽  
Evidence Network

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.

Reliability analysis for complex systems based on generalized stochastic petri nets and EDA approach considering common cause failure

2019 ◽  
Vol 37 (5) ◽  
pp. 1513-1530 ◽  
Author(s):  
Yining Zeng ◽  
Rongxing Duan ◽  
Shujuan Huang ◽  
Tao Feng
Keyword(s):  
Complex Systems ◽  
Reliability Analysis ◽  
Evaluation Method ◽  
Stochastic Petri Nets ◽  
Importance Measure ◽  
Dynamic Failure ◽  
Common Cause ◽  
Content Type ◽  
Common Cause Failure ◽  
Dynamic Fault Tree

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.

scholarly journals Effective sensor placement based on a VIKOR method considering common cause failure in the presence of epistemic uncertainty

2021 ◽  
Vol 23 (2) ◽  
pp. 253-262
Author(s):  
Rong-Xing Duan ◽  
Jie-Jun He ◽  
Tao Feng ◽  
Shu-Juan Huang ◽  
Li Chen
Keyword(s):  
System Reliability ◽  
Epistemic Uncertainty ◽  
Sensor Placement ◽  
Vikor Method ◽  
Common Cause ◽  
Reliability Indices ◽  
Common Cause Failure ◽  
Dynamic Fault Tree ◽  
Sensor Model

Owing to expensive cost and restricted structure, limited sensors are allowed to install in modern systems to monitor the working state, which can improve their availability. Therefore, an effective sensor placement method is presented based on a VIKOR algorithm considering common cause failure (CCF) under epistemic uncertainty in this paper. Specifically, a dynamic fault tree (DFT) is developed to build a fault model to simulate dynamic fault behaviors and some reliability indices are calculated using a dynamic evidence network (DEN). Furthermore, a VIKOR method is proposed to choose the possible sensor locations based on these indices. Besides, a sensor model is introduced by using a priority AND gate (PAND) to describe the failure sequence between a sensor and a component. All placement schemes can be enumerated when the number of sensors is given, and the largest system reliability is the best alternative among the placement schemes. Finally, a case study shows that CCF has some influence on sensor placement and cannot be neglected in the reliabilitybased sensor placement.

Optimal sensor placement based on multiattribute decision-making considering the common cause failure

2019 ◽  
Vol 233 (19-20) ◽  
pp. 7170-7182 ◽  
Author(s):  
Tao Feng ◽  
Rongxing Duan ◽  
Yanni Lin ◽  
Yining Zeng
Keyword(s):  
Decision Making ◽  
Sensor Placement ◽  
Fault Tree ◽  
Optimal Placement ◽  
Decision Matrix ◽  
Optimal Sensor Placement ◽  
Common Cause ◽  
Common Cause Failure ◽  
Dynamic Fault Tree ◽  
The Common

A new optimal sensor placement is developed to improve the efficiency of fault diagnosis based on multiattribute decision-making considering the common cause failure. The optimal placement scheme is selected based on the reliability of the top event on condition that the number of sensors is preset. Specifically, a β-factor model is introduced to deal with the common cause failure, and dynamic fault tree is used to describe the dynamic failure behaviors. Besides, a dynamic fault tree is converted into a dynamic Bayesian network to calculate the reliability parameters, which construct the decision matrix. Furthermore, an efficient TOPSIS algorithm is adopted to determine the potential locations of sensors. In addition, a diagnostic sensor model is developed to take into account the failure sequence between a sensor and a component using a priority AND gate, and the failure probability of the top event for all sensor placement scenarios is calculated to determine the optimal sensor placement. Finally, a case is provided to prove that the common cause failure has made a considerable impact on the sensor placement.

Fault-tolerant redundant repairable system with different failures and delays

2019 ◽  
Vol 37 (3) ◽  
pp. 1043-1071
Author(s):  
Chandra Shekhar ◽  
Amit Kumar ◽  
Shreekant Varshney ◽  
Sherif I. Ammar
Keyword(s):  
Reliability Analysis ◽  
Time Distribution ◽  
Service System ◽  
Fault Tolerant ◽  
Repair Time ◽  
Repairable System ◽  
Common Cause ◽  
Content Type ◽  
Common Cause Failure ◽  
N Stage

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.

scholarly journals Dynamic Diagnosis Strategy for Redundant Systems Based on Reliability Analysis and Sensors under Epistemic Uncertainty

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Rongxing Duan ◽  
Jinghui Fan
Keyword(s):  
Fuzzy Sets ◽  
Reliability Analysis ◽  
High Reliability ◽  
Epistemic Uncertainty ◽  
Fault Tree ◽  
Hybrid Approach ◽  
Qualitative Information ◽  
Quantitative Parameters ◽  
Dynamic Fault Tree ◽  
Dynamic Diagnosis

Fault tolerant technology is often used to improve systems reliability. However, high reliability makes it difficult to get sufficient fault samples, resulting in epistemic uncertainty, which increases significantly challenges in these systems diagnosis. A novel dynamic diagnosis strategy for complex systems is proposed to improve the diagnostic efficiency in the paper, which makes full use of dynamic fault tree, Bayesian networks (BN), fuzzy sets theory, and TOPSIS. Specifically, it uses a dynamic fault tree to model dynamic fault modes and evaluates the failure rates of the basic events using fuzzy sets to deal with epistemic uncertainty. Furthermore, it generates qualitative structure information based on zero-suppressed binary decision diagrams and calculates quantitative parameters provided by reliability analysis using a hybrid approach. Additionally, sensors data are incorporated to update the qualitative information and quantitative parameters. Qualitative information, quantitative parameters, and previous diagnosis result are taken into account to design a new dynamic diagnosis strategy which can locate the fault at the lowest cost. Finally, a case study is given to verify the developed approach and to demonstrate its effectiveness.

Evidential network-based failure analysis for systems suffering common cause failure and model parameter uncertainty

2018 ◽  
Vol 233 (6) ◽  
pp. 2225-2235 ◽  
Author(s):  
Lin Zuo ◽  
Tangfan Xiahou ◽  
Yu Liu
Keyword(s):  
Failure Analysis ◽  
Network Model ◽  
Parameter Uncertainty ◽  
Epistemic Uncertainty ◽  
Fault Tree ◽  
Common Cause ◽  
Common Cause Failure ◽  
Engineered Systems ◽  
Model Parameter Uncertainty ◽  
Evidential Network

The fault tree analysis has been extensively implemented in failure analysis of engineered systems. In most cases, the probabilities of basic events, e.g. components’ failures, are represented by crisp values in the fault tree analyses. However, due to lack of knowledge, scarcity of failure data, or vague judgments from experts, it may produce parameter uncertainty associated with degradation models of components/systems, and such model parameter uncertainty can be quantified by the epistemic uncertainty. In addition, the common cause failure, related to the simultaneous failures of two or more components caused by physical interactions or shared environments, often exists in advanced engineered systems and computing systems. In this paper, by considering both the common cause failure and the epistemic uncertainty associated with model parameters, an evidential network model embedded with common cause failure is proposed to facilitate system failure analysis. The detailed transformations from some logic gates of a fault tree to an evidential network model are given. Moreover, the conditional belief mass tables are constructed to quantify the dependency between the states of components and the entire system. An engineering case of an aero-engine oil system, together with comparative results, is presented to demonstrate the effectiveness of the proposed evidential network model.

Developing an Efficient Approach for Unmanned Aerial Vehicle Reliability Analysis

2020 ◽  
Author(s):  
Ahmad Khayyati ◽  
Mohammad Pourgol-Mohammad
Keyword(s):  
Reliability Analysis ◽  
Block Diagram ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Dynamic Fault Tree ◽  
Safety And Reliability ◽  
Time Dependencies ◽  
Aerial Vehicle ◽  
Redundant Component ◽  
High Level

Abstract Unmanned Aerial Vehicles (UAV) are increasingly get popularity in many applications. Their operation requires high level of safety and reliability to accomplish successful missions. In this study, the reliability was comparatively analyzed by different available approaches to select the efficient method. First, failure model of the system is developed. Then, three different scenarios are considered to study the effect of redundancies on the system reliability results. In the first scenario, there is no redundancy where in the second scenario there is only one redundant component and in the third scenario, there are three redundant components. Static reliability analysis such as Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), Markov Chain (MC), and Bayesian Networks (BN) are applied on proposed scenarios and results are obtained. Regarding to time dependencies between redundant components, a dynamic-based methodology is also developed in this study through applying Dynamic Fault Tree (DFT) analysis. Proposed static and dynamic approaches are applied on an UAV as a case study and results are discussed. Finally, characteristics of each methodology and related conditions are clarified for selecting the efficient reliability analysis approach.

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