Using Artificial Neural Network and Fuzzy Inference System Based Prediction to Improve Failure Mode and Effects Analysis: A Case Study of the Busbars Production
| Autor: | Saeed Na’amnh, Muath Bani Salim, István Husti, Miklós Daróczi |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
risk priority number (RPN)
Industry 4.0 Computer science Busbar Bioengineering busbars TP1-1185 computer.software_genre Software failure mode and effects analysis (FMEA) Chemical Engineering (miscellaneous) Production (economics) MATLAB QD1-999 fuzzy inference system (FIS) computer.programming_language Artificial neural network business.industry Process Chemistry and Technology Chemical technology artificial neural network (ANN) Chemistry Data mining business Risk assessment computer Failure mode and effects analysis |
| Zdroj: | Processes, Vol 9, Iss 1444, p 1444 (2021) Processes Volume 9 Issue 8 |
| ISSN: | 2227-9717 |
| Popis: | Nowadays, Busbars have been extensively used in electrical vehicle industry. Therefore, improving the risk assessment for the production could help to screen the associated failure and take necessary actions to minimize the risk. In this research, a fuzzy inference system (FIS) and artificial neural network (ANN) were used to avoid the shortcomings of the classical method by creating new models for risk assessment with higher accuracy. A dataset includes 58 samples are used to create the models. Mamdani fuzzy model and ANN model were developed using MATLAB software. The results showed that the proposed models give a higher level of accuracy compared to the classical method. Furthermore, a fuzzy model reveals that it is more precise and reliable than the ANN and classical models, especially in case of decision making. |
| Databáze: | OpenAIRE |
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