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Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario

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Abstract

Fault diagnosis and prognosis in mechanical systems have been researched and developed in the last few decades at a very rapid rate. However, owing to the high complexity of machine centers, research on improving the accuracy and reliability of fault diagnosis and prognosis via data mining remains a prominent issue in this field. This study investigates fault diagnosis and prognosis in machine centers based on data mining approaches to formulate a systematic approach and obtain knowledge for predictive maintenance in Industry 4.0 era. We introduce a system framework based on Industry 4.0 concepts, which includes the process of fault analysis and treatment for predictive maintenance in machine centers. The framework includes five modules: sensor selection and data acquisition module, data preprocessing module, data mining module, decision support module, and maintenance implementation module. Furthermore, a case study is presented to illustrate the application of the data mining methods for fault diagnosis and prognosis in machine centers as an Industry 4.0 scenario.

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Acknowledgements

This study was supported by a grant from Norway through the Norwegian Financial Mechanism 2009–2014, in the frame of the Green Industry Innovation Programme Bulgaria. Moreover, the authors gratefully thank Inter Consult Bulgaria (ICB) and Kongsberg Terotech (KTT) for their assistance in the software and data sources used in this study.

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway

    Zhe Li & Ke-Sheng Wang

  2. School of Business, Plymouth University, Plymouth, UK

    Yi Wang

Authors
  1. Zhe Li
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  2. Yi Wang
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  3. Ke-Sheng Wang
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Correspondence to Zhe Li.

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Li, Z., Wang, Y. & Wang, KS. Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Adv. Manuf. 5, 377–387 (2017). https://doi.org/10.1007/s40436-017-0203-8

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  • DOI: https://doi.org/10.1007/s40436-017-0203-8

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