Using Deep Neural Networks to Evaluate the System Reliability of Manufacturing Networks

doi:10.23940/ijpe.21.07.p4.600608

Abstract

Abstract: This paper focuses on the system reliability evaluation for a stochastic-flow manufacturing network by a Deep Learning approach. Knowing the capability of the manufacturing system in real time is a critical issue because the manufacturing industry conducts mass production through automated machines. In existing algorithms, system reliability cannot be calculated in a short time when the network model is complex. Hence, an efficient algorithm based on the Deep Neural Network is developed to predict the system reliability instantly. According to the experimental results, the proposed algorithm can predict system reliability with a Root-Mean-Square Error of 0.002. Compared with existing algorithms, the proposed algorithm can evaluate the reliability of a system in only one-tenth of the time.

Key words: manufacturing network, system reliability, deep learning, deep neural network

Yi-Fan Chen, Yi-Kuei Lin, and Cheng-Fu Huang. Using Deep Neural Networks to Evaluate the System Reliability of Manufacturing Networks [J]. Int J Performability Eng, 2021, 17(7): 600-608.

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