Int J Performability Eng ›› 2021, Vol. 17 ›› Issue (7): 600-608.doi: 10.23940/ijpe.21.07.p4.600608

Previous Articles     Next Articles

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

Yi-Fan Chena, Yi-Kuei Lina,b,c,d,*, and Cheng-Fu Huange   

  1. aDepartment of Industrial Engineering and Management, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan;
    bDepartment of Business Administration, Asia University, Taichung, 413, Taiwan;
    cDepartment of Medical Research, China Medical University Hospital, China Medical University, Taichung, 404, Taiwan;
    dDepartment of Business Administration, Chaoyang University of Technology, Taichung, 413, Taiwan;
    eDepartment of Business Administration, Feng Chia University, Taichung, 407, Taiwan
  • Contact: * E-mail address:

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