Combinatorial Reliability Evaluation of Multi-State System with Epistemic Uncertainty

Yang, Jinjin; Xing, Liudong; Wang, Yujie; He, Lijuan

doi:10.33889/ijmems.2022.7.3.021

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…A reliability and risk analysis based on epistemic uncertainty was performed on the model [17][18][19][20]. Uncertainty modelling was performed using Monte Carlo simulations to model the uncertainty of payment failure by randomly sampling 5000 data from the statistics of payment failure reasons to obtain a sample set of random payment failure reasons.…”

Section: Analysis Of Reliability and Risksmentioning

confidence: 99%

An Improved LSTM-Based Failure Classification Model for Financial Companies Using Natural Language Processing

2023

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The Korean e-commerce market represents a large percentage of the global retail distribution market, a market that continues to grow each year, and online payments are rapidly becoming a mainstream payment method. As e-commerce becomes more active, many companies that support electronic payments are increasing the number of franchisees. Electronic payments have become an indispensable part of people’s lives. However, the types of statistical information on the results of electronic payment transactions are not consistent across companies, and it is difficult to automatically determine the error status of a transaction if no one directly confirms the error messages generated during payment. To address these issues, we propose an optimized LSTM model. In this study, we classify the error content in statistical information based on natural language processing to determine the error status of the current failed transaction. We collected 11,865 response messages from various vendors and financial companies and labelled them with an LSTM classifier model to create a dataset. We then trained this dataset with simple RNN, LSTM, and GRU models and compared their performance. The results show that the optimized LSTM model with the attention layer added to the dropout layer and the bidirectional recursive layer achieves an accuracy of about 92% or more. When the model is applied to e-commerce services, any error in the transaction status of the system can be automatically detected by the model.

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Section: Analysis Of Reliability and Risksmentioning

confidence: 99%

An Improved LSTM-Based Failure Classification Model for Financial Companies Using Natural Language Processing

2023

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“…Furthermore, the authors also proposed a novel collocation method for the appraisal of the fuzzy reliability of systems. Yang et al [14] evaluated the reliability of multi-state systems (MSS) based on multi-valued decision diagrams under epistemic uncertainty with the help of fuzzy set theory and interval theory. Furthermore, the technique is validated using a case study of a high-speed train bogie system.…”

Section: Reliability Evaluation Using Fuzzy Set Theorymentioning

confidence: 99%

Statistical Fuzzy Reliability Assessment of a Blended System

Chachra

Kumar

Ram

et al. 2023

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Fuzzy sets have been proven to constitute an asset in the evolution of reliability theory in recent decades. Their contribution in addressing the possibility of errors, insufficiency of data, randomness, or fuzziness, either in the system or in the accumulation of any data for the respective system, which is overlooked in the traditional reliability assessment, seems to be quite crucial. The present work deals with the statistical fuzzy reliability evaluation of a blended system that comprises two subsystems. One system contains two components aligned in a parallel configuration, and the other is a 3-out-of-5 system. The reliability of this model is assessed using two approaches to intuitionistic fuzzy sets (IFS), namely, traditional IFS and interval-valued intuitionistic fuzzy sets (IVIFS). Three cases are considered in each approach, which are compared individually as well as with each other. It was established that the IVIFS yield better results than the IFS. The obtained results are displayed in both tabular and graphical forms for better assessment.

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“…Garg (2021) determined a bi-objective reliability-cost problem for a series-parallel system by using an interactive approach. Yang et al (2022) addressed the problem of reliability evaluation of multi-state systems with apparent uncertainty based on multi-valued decision diagrams by combining interval theory and fuzzy set theory. For complex mechatronic systems with hierarchical, nonlinear, dependent, uncertain and stochastic properties, Chen et al (2023) used the Wiener process model to describe the continuous time degradation process and developed the copula hierarchical Bayesian network for reliability evaluation.…”

Section: Introductionmentioning

confidence: 99%

Reliability Evaluation and Prediction Method with Small Samples

Dui

Dong

Tao

2023

Int. j. math. eng. manag. sci.

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How to accurately evaluate and predict the degradation state of the components with small samples is a critical and practical problem. To address the problems of unknown degradation state of components, difficulty in obtaining relevant environmental data and small sample size in the field of reliability prediction, a reliability evaluation and prediction method based on Cox model and 1D CNN-BiLSTM model is proposed in this paper. Taking the historical fault data of six components of a typical load-haul-dump (LHD) machine as an example, a reliability evaluation method based on Cox model with small sample size is applied by comparing the reliability evaluation models such as logistic regression (LR) model, support vector machine (SVM) model and back propagation neural network (BPNN) model in a comprehensive manner. On this basis, a reliability prediction method based on one-dimensional convolutional neural network-bi-directional long and short-term memory network (1D CNN-BiLSTM) is applied with the objective of minimizing the prediction error. The applicability as well as the effectiveness of the proposed model is verified by comparing typical time series prediction models such as the autoregressive integrated moving average (ARIMA) model and multiple linear regression (MLR). The experimental results show that the proposed model is valuable for the development of reliability plans and for the implementation of reliability maintenance activities.

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Combinatorial Reliability Evaluation of Multi-State System with Epistemic Uncertainty

Cited by 6 publications

References 38 publications

An Improved LSTM-Based Failure Classification Model for Financial Companies Using Natural Language Processing

An Improved LSTM-Based Failure Classification Model for Financial Companies Using Natural Language Processing

Statistical Fuzzy Reliability Assessment of a Blended System

Reliability Evaluation and Prediction Method with Small Samples

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