Journal of Quality Engineering and Management

Fuzzy logic and artificial neural network hybrid modeling to predict machine failure in order to increase productivity

Document Type : Original Article

Authors

Parviz Choopankari 1
amir azizi 2
mohammad javad ershadi 3

1 Industrial Engineering, Science and Research Unit, Islamic Azad University, Tehran, Iran

2 Assistant professor, faculty member of Islamic Azad University, Faculty of Industrial Engineering, Science and Research Unit

3 Irandoc Institute

https://doi.org/10.48313/jqem.2022.166513
Abstract
In this research, a hybrid approach based on fuzzy logic and artificial neural network is presented to predict the failure of machines in order to increase productivity. The subject of this research is one of the factories of the automobile industry named Diaco Ide Aria, which operates in the field of automobile parts production. Preventive maintenance requires correct prediction of breakdowns and accidents, equipment and machines so that productivity can be increased by timely and correct maintenance of machines as well as fixing defects and breakdowns. To model the multi-layer perceptron fuzzy-neural network (MLP), first, 100 failures and stops were collected in a period of 15 months and then entered into MATLAB software. The obtained results show that the implementation of fuzzy-neural network and the prediction of machine failure time has reduced the duration and cost of repairs. Therefore, the working time and accessibility of the machines increased and ultimately increased the productivity by 57%, also, the accuracy of the developed neural-fuzzy model was estimated at 94%.

Keywords

Productivity
prediction
neural network
fuzzylogic
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Journal of Quality Engineering and Management
Volume 12, Issue 1
Spring 2022
Pages 69-86

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