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Machines 2018, 6(2), 17; https://doi.org/10.3390/machines6020017

Development of a Methodology for Condition-Based Maintenance in a Large-Scale Application Field

1
Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia 42122, Italy
2
Tetra Pak Packaging Solutions SpA, Modena 41123, Italy
*
Author to whom correspondence should be addressed.
Received: 27 February 2018 / Revised: 9 April 2018 / Accepted: 10 April 2018 / Published: 16 April 2018
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
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Abstract

This paper describes a methodology, developed by the authors, for condition monitoring and diagnostics of several critical components in the large-scale applications with machines. For industry, the main target of condition monitoring is to prevent the machine stopping suddenly and thus avoid economic losses due to lack of production. Once the target is reached at a local level, usually through an R&D project, the extension to a large-scale market gives rise to new goals, such as low computational costs for analysis, easily interpretable results by local technicians, collection of data from worldwide machine installations, and the development of historical datasets to improve methodology, etc. This paper details an approach to condition monitoring, developed together with a multinational corporation, that covers all the critical points mentioned above. View Full-Text
Keywords: condition monitoring; data-driven diagnostics; model-based diagnostics condition monitoring; data-driven diagnostics; model-based diagnostics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Cocconcelli, M.; Capelli, L.; Cavalaglio Camargo Molano, J.; Borghi, D. Development of a Methodology for Condition-Based Maintenance in a Large-Scale Application Field. Machines 2018, 6, 17.

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