Reliability Assessment of Random Uncertain Multi-State Systems

Hu, Linmin; Yue, Dequan; Zhao, Guoxi

doi:10.1109/access.2019.2898912

Cited by 16 publications

(13 citation statements)

References 34 publications

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“…Thus, uncertainty theory is more suitable for situations with limited/scarce data for statistical analysis. It has been introduced to various domains, such as reliability analysis [19] [20], risk analysis [21], supply chain [22], accelerated degradation testing [23], data development analysis [24], etc.…”

Section: Introductionmentioning

confidence: 99%

First-Order Uncertain Hidden Semi-Markov Process for Failure Prognostics With Scarce Data

Liu

2020

IEEE Access

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Failure prognostics aims at predicting the object equipment's future degradation trend and derives the remaining useful life with a predefined failure threshold. Hidden semi-Markov process (HSMP) is widely adopted for failure prognostics of degradation process with discrete states. The effective estimation of the holding time distribution on each degradation state is of critical importance for the prediction performance of a HSMP model. The distributions are generally estimated with frequencies-based probabilities given a large amount of degradation data. In practical engineering applications, it is difficult to collect enough data and the data can even be scarce. In such situations, the estimated distributions are no longer reliable. First-order uncertain hidden semi-Markov process (1-UHSMP) based on uncertain statistics is defined in this work. The holding time distributions in 1-UHSMP are described with uncertainty theory and are adaptively updated with conditional uncertainty distributions given observations related to the true degradation states. Analytical expressions are derived for the expected remaining useful life for 1-UHSMP with regular uncertainty distributions, i.e. normal and linear uncertainty distribution. The proposed method can build a degradation model from scarce data and derive adaptively the remaining useful life with associated uncertainty interval. A case study concerning centrifugal pumps in a nuclear power plant is considered to verify the effectiveness of 1-UHSMP.

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Section: Introductionmentioning

confidence: 99%

First-Order Uncertain Hidden Semi-Markov Process for Failure Prognostics With Scarce Data

Liu

2020

IEEE Access

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“…An intelligent manufacturing system is a typical multi-state system [9], [10]. Due to the complex function and structure of manufacturing system, the binary state reliability theory based on the criteria of normal function and fault cannot effectively reflect a system's actual state, nor can it satisfy the reliability evaluation requirements of a complex manufacturing system.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling Method of Mission Reliability of a Multi-State Manufacturing System With Multiple Production Lines

Zhang

Wang

et al. 2020

IEEE Access

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Mission reliability could systematically characterize the running state of a manufacturing system and the rationality of production task setting in advance. However, few studies can realize the mission reliability modeling, which integrates production scheduling based on fully considering the complexity, dynamics, and multi-state characteristics of a manufacturing system. Thus, a dynamic method for modeling the mission reliability of a multi-state manufacturing system with multiple production lines is proposed in this paper. First, the multi-state characteristic of manufacturing systems operation is analyzed. On this basis, considering the composition of manufacturing systems, the connotation and modeling mechanism of the mission reliability of manufacturing systems are proposed. Second, combining the discretization of machine performance states and dynamic continuity of the performance degradation process, the dynamic modeling method of multi-state machine performance and buffer usage are respectively discussed. The transfer evolution of task demands in the production line is analyzed, and the decomposition method of task scheduling and load mapping in multiple production lines is provided. Third, considering the characteristics of multiple production lines and the buffer of a manufacturing system, taking production tasks as the core and integrating production scheduling, a modeling method for the mission reliability of manufacturing systems is proposed. Finally, the effectiveness of the proposed approach is verified using a case study on the modeling of the mission reliability of a manufacturing system of aerospace parts.

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“…They used a Bayesian-network-based parameterplanning model employing fuzzy mathematics and the gray theory. Hu et al [18] employed the probability and uncertainty theories to define MSS features with random uncertainty. In their research, the state probability and system-component performances were considered nonprobabilistic uncertain variables, and subsequently, the uncertain UGF was used to evaluate the MSS reliability with random uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, the FMSS reliability models reported in [13,15,16,[18][19][20] are based on the approach proposed by Ding et al [10], whereas those presented in [12] and [13] are an application of the approach presented in [11] involving the use of an FMSS reliability index to compensate for the drawbacks of the method presented in [10] that utilizes linear TFN to quantify circumstances with non-probabilistic uncertainties. The approach proposed in [11] considers the αcut value in determining the extent to which the stateperformance membership function meets the mission demand.…”

Section: Introductionmentioning

confidence: 99%

Improved Availability Index for Repairable Fuzzy Multi-State Systems

2020

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Fuzzy multi-state systems (FMSSs) comprise FMS components (FMSCs) aims at analyzing non-probabilistic uncertain circumstances defined by fuzzy state-transition rate and performance numbers. The triangular fuzzy number that features intuitive and algebraic operations is widely used for quantifying fuzziness. This study aims to overcome the drawbacks of the existing FMSS availability index that either does not conform to a normal convex set or is inconsistent with the crisp-circumstance multi-state system (MSS) availability beyond summation over state probabilities that do no equal unity. The proposed method first evaluates the instantaneous state probability (ISP) of FMSCs by establishing the FMSCs Markov model. Subsequently, the FMSSs ISP is evaluated by integrating the FMSCs ISP using the fuzzy universal generating function based on the system-structure function. A "constrained nonlinear parameter programming model" has been developed to evaluate the improved FMSSs availability. The corresponding availability index features the normal convex set whilst fulfilling the MSS theory, which requires state probabilities to equal unity during availability evaluation. The effectiveness of the proposed approach has been verified using an illustrated community-based smart-grid system comprising three FMSCs-solar-and wind-energy systems as well as diesel-generator equipment. Moreover, the results of an independent sensitive analysis provide further insights into the improved FMSSs availability with regard to the time and circumstance fuzziness.

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Reliability Assessment of Random Uncertain Multi-State Systems

Cited by 16 publications

References 34 publications

First-Order Uncertain Hidden Semi-Markov Process for Failure Prognostics With Scarce Data

First-Order Uncertain Hidden Semi-Markov Process for Failure Prognostics With Scarce Data

Dynamic Modeling Method of Mission Reliability of a Multi-State Manufacturing System With Multiple Production Lines

Improved Availability Index for Repairable Fuzzy Multi-State Systems

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