University of Oulu

Koistinen, Antti (2018) Big Data for predictive maintenance of industrial machinery. In: Fifteenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM 2018/MFPT 2018) : Nottingham, United Kingdom 10 - 12 September 2018. pp. 405-415.

Big Data for predictive maintenance of industrial machinery

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Author: Koistinen, Antti1
Organizations: 1Control Engineering, Faculty of Technology, P.O. Box 4300, FI-90014 University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe201901101862
Language: English
Published: British Institute of Non-Destructive Testing, 2018
Publish Date: 2019-01-10
Description:

Abstract

The operation of industrial manufacturing processes can suffer greatly when critical components fail suddenly. Large manufacturing processes can have plenty of critical components whose failure can interfere with the process operation. Typically these parts are changed periodically according to preventive maintenance strategy. Industry is eager to move towards predictive maintenance in order to make savings in spare parts and lower downtime. Predictive maintenance requires several measurement campaigns from a single part in order to make a working model or finding condition thresholds. A single measurement campaign from a certain part can take lots of time and give limited information about developing condition in certain environment. Multiplying the amount of this measured data leads to a more reliable estimate for the aspects affecting the condition and thresholds. The idea is to gather condition monitoring data from several similar machines or machine parts from a wide range of different environmental and stress conditions. This data can be used to generate models for several varying fault types. Data used for this system can include condition monitoring data from the target, automation system data describing operating conditions, metadata for describing environmental factors and maintenance reports in standardized form, including pictures of faults and events.

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ISBN Print: 978-1-5108-7135-9
Pages: 405 - 415
Host publication: Fifteenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM 2018/MFPT 2018) : Nottingham, United Kingdom 10 - 12 September 2018
Conference: International Conference on Condition Monitoring and Machinert Failure Prevention Technologies
Type of Publication: A4 Article in conference proceedings
Field of Science: 222 Other engineering and technologies
Subjects:
Copyright information: © 2018 The Author and British Institute of Non-Destructive Testing. Published in this repository with the kind permission of the publisher.