Key Process and Quality Characteristic Identification for Manufacturing Systems using Dynamic Weighting Function and D-S Evidence Theory

Yuan, Qing-wen

doi:10.23940/ijpe.18.08.p1.16511665

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…In the D-S theory of evidence [24][25][26][27], the set of all possible propositions is defined as a universe X={x1,x2,…,xn}, and the basic belief assignment function is defined as m:…”

Section: Decision-level Fusion Based On D-s Theory Of Evidencementioning

confidence: 99%

A Gas Outburst Prediction Model Based on Data Mining and Information Fusion

He¹

2019

RIA

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The gas outburst in coalmines is influenced by multiple factors. These influencing factors are highly uncertain and have complex nonlinear relationships. Considering these features, this paper puts forward a gas outburst prediction model based on data mining and information fusion. On the feature level, the backpropagation neural network (BPNN) was selected to set up a gas outburst identification model, thanks to its strong self-learning ability, and then optimized by the improved particle swarm optimization (IPSO); then, the outputs of the optimized BPNN were taken as the identification results, and used to establish a feature database. On the decision level, the Dempster-Shafter (D-S) theory of evidence was introduced to fuse the identification results in the time domain and the spatial domain, and make decisions on the gas state of the coalmine based on the fused data. Finally, the proposed model was applied the predict the gas outburst in a coalmining area of a coalmine in Shanxi Province, China, using the data collected from the workface, intake airway, return airway and transport roadway. Our model fuses the data on two layers, namely, the time domain and the spatial domain, and reduces the prediction uncertainty to zero. The results show that our model can optimize the prediction parameters, enhance the accuracy of gas monitoring information, and improve the correctness of decisions concerning gas outburst in the coalmine.

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“…In the D-S theory of evidence [24][25][26][27], the set of all possible propositions is defined as a universe X={x1,x2,…,xn}, and the basic belief assignment function is defined as m:…”

Section: Decision-level Fusion Based On D-s Theory Of Evidencementioning

confidence: 99%

A Gas Outburst Prediction Model Based on Data Mining and Information Fusion

He¹

2019

RIA

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show abstract

“…Then, based on the adjusted feature data, the process results were expressed by fuzzy triangular numbers. Finally, one can identify critical procedure based on D-S evidence fusion rules [21]. Although this method considers the factors affecting the procedure, it lacks the analysis of product quality.…”

Section: Introductionmentioning

confidence: 99%

Critical Procedure Identification Method Considering the Key Quality Characteristics of the Product Manufacturing Process

Gao

Zhou

et al. 2022

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The product’s manufacturing process has an evident influence on product quality. In order to control the quality and identify the critical procedure of the product manufacturing process reasonably and effectively, a method combining genetic back-propagation (BP) neural network algorithm and grey relational analysis is proposed. Firstly, the genetic BP neural network algorithm is used to obtain the key quality characteristics (KQCs) in the product manufacturing process. At the same time, considering the three factors that have an essential impact on the quality of the procedures, the grey correlation analysis method is used to establish the correlation scoring matrix between the procedure and the KQCs to calculate the criticality of each procedure. Finally, taking the manufacturing process of the evaporator as a case, the application process of this method is introduced, and four critical procedures are identified. It provides a reference for the procedure quality control and improvement of enterprise in the future.

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Key Process and Quality Characteristic Identification for Manufacturing Systems using Dynamic Weighting Function and D-S Evidence Theory

Cited by 2 publications

References 32 publications

A Gas Outburst Prediction Model Based on Data Mining and Information Fusion

A Gas Outburst Prediction Model Based on Data Mining and Information Fusion

Critical Procedure Identification Method Considering the Key Quality Characteristics of the Product Manufacturing Process

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