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A quantum multi-agent based neural network model for failure prediction

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Abstract

An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique for accurate failure prognosis based on back propagation neural network and quantum multi-agent algorithm. Inspired by the extensive research of quantum computing theory and multi-agent systems, the technique employs a quantum multi-agent strategy, with the main characteristics of quantum agent representation and several operations including fitness evaluation, cooperation, crossover and mutation, for parameters optimization of neural network to avoid the deficiencies such as slow convergence and liability of getting stuck to local minima. To validate the feasibility of the proposed approach, several numerical approximation experiments were firstly designed, after which real vibrational data of bearings from the Laboratory of Cincinnati University were analyzed and used to assess the health condition for a given future point. The results were rather encouraging and indicated that the presented forecasting method has the potential to be utilized as an estimation tool for failure prediction in industrial machinery.

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Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China

    Wei Wu, Min Liu & Qing Liu

  2. The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, 200092, China

    Weiming Shen

Authors
  1. Wei Wu
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  2. Min Liu
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  3. Qing Liu
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  4. Weiming Shen
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Corresponding authors

Correspondence to Min Liu or Weiming Shen.

Additional information

Wei Wu is an MSc student at the School of Electronic and Information Engineering, Tongji University, Shanghai, China. She received the BSc degree in automation from Shanghai Second Polytechnic University, China, in 2013. Her research interests include computational intelligence and equipment maintenance.

Min Liu is a full professor of Tongji University. He received the PhD degree from Zhejiang University, China, in 1999. He joined the School of Electronic and Information Engineering, Tongji University in 2004, and is currently a member of the Research Center for Computer Integrated Manufacturing System. He has been working on intelligent maintenance and optimal allocation of service for about 15 years. In these areas, he has published over 70 papers in leading international journals or conference proceedings, and holds 4 patents. He has received various honors, including the second prize of Shanghai scientific and technological progress award, the government subsidy of Shanghai and the advanced worker for 15th anniversary celebration of the national 863 project.

Qing Liu is an MSc student at the School of Electronic and Information Engineering, Tongji University, Shanghai, China. He received the BSc degree from the Changshu Institute of Technology, Jiangsu, China, in 2014. He is currently studying in the Lab of Intelligent Maintenance System & Service at Tongji University.

Weiming Shen is a Senior Research Scientist at National Research Council Canada and an Adjunct Professor at Tongji University, China. He received his BSc (1983) and MSc (1986) degrees from Northern (Beijing) Jiaotong University, China and his PhD degree (1996) from the University of Technology of Compiègne, France. His research interests include collaborative design and manufacturing, production scheduling, service-oriented computing, agent-based collaboration technologies and applications. He has published several books and over 300 papers in scientific journals and international conferences in the related areas. Dr. Shen is an Associate Editor or a member of editorial board for several international journals, including IEEE Transactions on Automation Science and Engineering, IEEE Transactions on SMC: Systems; Advanced Engineering Informatics, Service Oriented Computing and Applications, and Intelligent Buildings International.

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Wu, W., Liu, M., Liu, Q. et al. A quantum multi-agent based neural network model for failure prediction. J. Syst. Sci. Syst. Eng. 25, 210–228 (2016). https://doi.org/10.1007/s11518-016-5308-2

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  • DOI: https://doi.org/10.1007/s11518-016-5308-2

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