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Fractal Analysis Application Outlook for Improving Process Monitoring and Machine Maintenance in Manufacturing 4.0

Department of Mechanical Engineering, Polytechnique Montreal, 2900 Blvd. Edouard-Montpetit, Montreal, QC H3T 1J4, Canada
Department of Mechanical Engineering, École de Technologie Supérieure, 1100 Notre-Dame St W, Montreal, QC H3C 1K3, Canada
Author to whom correspondence should be addressed.
J. Manuf. Mater. Process. 2018, 2(3), 62;
Received: 28 July 2018 / Revised: 7 September 2018 / Accepted: 8 September 2018 / Published: 10 September 2018
(This article belongs to the Special Issue Smart Manufacturing Processes in the Context of Industry 4.0)
Industry 4.0 has been advertised for a decade as the next disruptive evolution for production. It relies on automation growth and particularly on data exchange using numerous sensors in order to develop faster production with tight monitoring. The huge amount of data generated by clouds of sensors during production is often used to feed machine learning systems in order to detect faults, monitor and find possible ways for improvement. However, the artificial intelligence within machine learning requires finding and selecting key features, such as average and root mean square. While current machine learning has already proven its use in diverse applications, its efficiency could be further improved by generating better characteristics such as fractal parameters. In this paper, fractal analysis concept is presented and its current and future applications in machining are discussed. This sensitive and robust technique is already extracting high performance key features that could fill in monitoring and prediction systems. On top of improving features selection and, thus, improving the overall performance of monitoring and predictive systems in machining, this could lead to a more rapid artificial intelligence implementation into manufacturing. View Full-Text
Keywords: fractal analysis; machining; process monitoring; maintenance; sensor fractal analysis; machining; process monitoring; maintenance; sensor
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Rimpault, X.; Balazinski, M.; Chatelain, J.-F. Fractal Analysis Application Outlook for Improving Process Monitoring and Machine Maintenance in Manufacturing 4.0. J. Manuf. Mater. Process. 2018, 2, 62.

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