System reliability prediction model based on evidential reasoning algorithm with nonlinear optimization

Hu, Changhua; Si, Xiaosheng; Yang, Jian‐Bo

doi:10.1016/j.eswa.2009.08.024

Cited by 44 publications

(14 citation statements)

References 56 publications

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“…A C C E P T E D ACCEPTED MANUSCRIPT 3 behaviour using various qualitative and quantitative approaches (Adamyan & David, 2004;Aksu & Osman, 2006;Hauptmanns, 2011;Hu, Si, & Yang, 2010;Cai, 1996;Modarres & Kaminski, 1999;Vallem & Saravannan, 2011).…”

Section: A N U S C R I P Tmentioning

confidence: 99%

Integrated framework for behaviour analysis in a process plant

Panchal

Kumar

2016

Journal of Loss Prevention in the Process Industries

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Section: A N U S C R I P Tmentioning

confidence: 99%

Integrated framework for behaviour analysis in a process plant

Panchal

Kumar

2016

Journal of Loss Prevention in the Process Industries

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“…For specific details, the reader is referred to [50]. Once the inputoutput data become available, the training data sets can be re-constructed as (X(t m ), y(t m )), m = 1, .…”

Section: Optimal Learning Algorithms For Training Er-based Predictionmentioning

confidence: 99%

Fault prediction model based on evidential reasoning approach

Hu²,

Zhou

2010

Sci. China Inf. Sci.

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In order to deal with fault prediction problems that involve both quantitative and qualitative information for nonlinear complex system, a new fault prediction model is established based on the evidential reasoning (ER) approach, and an optimal learning algorithm for training ER-based prediction model is presented based on the mean square error (MSE) criterion. This prediction model inherits the advantages of ER approach, which can deal with precise data, incomplete data and fuzzy data with nonlinear characteristic. In this model, the input signals transformed using rule based information transformation technique, are aggregated by analytical ER approach, and then the outputs of prediction model are constructed according to the types of system outputs. In addition, two fault decision criteria are defined to conduct fault identification. To overcome the difficulty in determining model parameters accurately and subjectively, a nonlinear optimization model is constructed and the optimal parameters are obtained. Two experimental studies are conducted to evaluate the performance of the proposed model. The results show that the established prediction model and the presented parameters optimization methods can deal with fault prediction problem effectively.

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“…Zhou further studied the ER approach under both fuzzy weights and utilities under group decision making situation [5], [11] . In recent years, the ER approach has been developed in many applications, for instance, environmental impact assessment [7] , product project screening [16] , R&D projects assessment [4] , safety analysis [17] , consumer preferences extraction [18]- [20] , clinical risk assessment [21] , project risk analysis [22] , system reliability prediction [23] , new product design assessment [24] , failure mode and effects analysis [25] , highway express passenger transportation company evaluation [26] , the performance of VMI alliance [5] and so on.…”

Section: Introductionmentioning

confidence: 99%

Evidential reasoning approach for MADM based on incomplete interval value

Zhou

Liu

Yang

2017

IFS

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In a multiple attribute decision making (MADM) problem, quantitative and qualitative attributes can be assessed by numerical values and subjective judgements. Numerical value can be accurate or uncertain, while qualitative attribute could be evaluated by linguistic variables. The evidential reasoning (ER) approach provides a process for dealing with MADM problems of both a quantitative and qualitative nature under uncertainty. In the existing ER approach, however, only benefit and cost attributes are considered in the evidence combination process. In this paper, deviated and fixed attributes are introduced into MADM and the frames of discernment for representing these two kinds of attributes are proposed. The transformation rules from the assessment values of deviated and fixed attributes to belief degrees in the ER structure are then studied. In the existing MADM literature, interval value is assumed to be uniformly distributed and complete in the sense that any value in the interval is equally likely and that the probabilities of values in the interval being taken sum to one. In real life decision situations, however, interval value could be incomplete in that the sum of probabilities of values in the interval being taken can be less than one. In this paper, incomplete interval value is introduced to a decision making process, and the transformation rule of incomplete interval value to belief degrees in the frame of discernment is analyzed. The characteristics of the transformation rule are studied. Two case studies are provided to illustrate the implementation of the proposed new concept and technique and the potential in supporting MADM under uncertainty.

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System reliability prediction model based on evidential reasoning algorithm with nonlinear optimization

Cited by 44 publications

References 56 publications

Integrated framework for behaviour analysis in a process plant

Integrated framework for behaviour analysis in a process plant

Fault prediction model based on evidential reasoning approach

Evidential reasoning approach for MADM based on incomplete interval value

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