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

doi:10.1007/s40436-017-0203-8

Abstract

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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0203-8/fulltext.html

Key words: Data mining (DM), Machine centers, Predictive maintenance, Industry 4.0

Zhe Li, Yi Wang, Ke-Sheng Wang. Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario[J]. Advances in Manufacturing, 2017, 5(4): 377-387.

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