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Achieving Optimised Infrared Thermography in Innovative Asset Management

Roderick Thomas Orcid Logo

Advances in Asset Management and Condition Monitoring, Pages: 1553 - 1566

Swansea University Author: Roderick Thomas Orcid Logo

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Abstract

Optimised Asset Management in recent years has embraced a rapid diffusion of innovation and disruptive technology, especially with reference to infrared thermography. The integration of this technology and associated communication technologies are improving operational aspects of industry, including...

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Published in: Advances in Asset Management and Condition Monitoring
ISBN: 9783030577445 9783030577452
ISSN: 2190-3018 2190-3026
Published: Cham Springer International Publishing 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa56754
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first_indexed 2021-04-28T11:16:31Z
last_indexed 2022-06-16T03:15:41Z
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spelling 2022-06-15T15:41:30.0646615 v2 56754 2021-04-28 Achieving Optimised Infrared Thermography in Innovative Asset Management 891091891b6eee412668ae216f713312 0000-0002-2792-1251 Roderick Thomas Roderick Thomas true false 2021-04-28 BBU Optimised Asset Management in recent years has embraced a rapid diffusion of innovation and disruptive technology, especially with reference to infrared thermography. The integration of this technology and associated communication technologies are improving operational aspects of industry, including the prediction of machine and component failures and as a diagnostic tool in medicine. Most machines in the future will be connected to the Internet of Things (IoT) which will be the gateway to communicating with intelligent assets with self-diagnosing capabilities and expert systems. Asset connectivity and predictive analytics will discern patterns and algorithms leading optimised plant production, and enhanced energy efficiency particularly with reference to machines. Machine learning models will indicate future operation on a real-time basis using big data libraries, tabular databases with particular reference to condition monitoring. This paper concentrates on the application of qualitative and quantitative portable infrared thermography. Successful implementation of an intelligent portable infrared thermography system requires an understanding of the industrial process; the machine operation, its surroundings, and the dynamics of infrared radiation. Optimising and integrating infrared into an asset management subsystem requires correct monitoring equipment selection and accurate data collection including; Optimum radiometer wavelength, background characterization, spatial and thermal resolution and emissivity. This paper concludes with an industrial atlas of infrared normal and abnormal images, which are a useful reference in determining the various conditions in medical diagnostics with the primary intention to identify early onset of problems as part of an optimised asset management system. Conference Paper/Proceeding/Abstract Advances in Asset Management and Condition Monitoring 1553 1566 Springer International Publishing Cham 9783030577445 9783030577452 2190-3018 2190-3026 28 8 2020 2020-08-28 10.1007/978-3-030-57745-2_126 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2022-06-15T15:41:30.0646615 2021-04-28T12:11:40.4289728 School of Management School of Management Roderick Thomas 0000-0002-2792-1251 1
title Achieving Optimised Infrared Thermography in Innovative Asset Management
spellingShingle Achieving Optimised Infrared Thermography in Innovative Asset Management
Roderick Thomas
title_short Achieving Optimised Infrared Thermography in Innovative Asset Management
title_full Achieving Optimised Infrared Thermography in Innovative Asset Management
title_fullStr Achieving Optimised Infrared Thermography in Innovative Asset Management
title_full_unstemmed Achieving Optimised Infrared Thermography in Innovative Asset Management
title_sort Achieving Optimised Infrared Thermography in Innovative Asset Management
author_id_str_mv 891091891b6eee412668ae216f713312
author_id_fullname_str_mv 891091891b6eee412668ae216f713312_***_Roderick Thomas
author Roderick Thomas
author2 Roderick Thomas
format Conference Paper/Proceeding/Abstract
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publishDate 2020
institution Swansea University
isbn 9783030577445
9783030577452
issn 2190-3018
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doi_str_mv 10.1007/978-3-030-57745-2_126
publisher Springer International Publishing
college_str School of Management
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hierarchy_top_title School of Management
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hierarchy_parent_title School of Management
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description Optimised Asset Management in recent years has embraced a rapid diffusion of innovation and disruptive technology, especially with reference to infrared thermography. The integration of this technology and associated communication technologies are improving operational aspects of industry, including the prediction of machine and component failures and as a diagnostic tool in medicine. Most machines in the future will be connected to the Internet of Things (IoT) which will be the gateway to communicating with intelligent assets with self-diagnosing capabilities and expert systems. Asset connectivity and predictive analytics will discern patterns and algorithms leading optimised plant production, and enhanced energy efficiency particularly with reference to machines. Machine learning models will indicate future operation on a real-time basis using big data libraries, tabular databases with particular reference to condition monitoring. This paper concentrates on the application of qualitative and quantitative portable infrared thermography. Successful implementation of an intelligent portable infrared thermography system requires an understanding of the industrial process; the machine operation, its surroundings, and the dynamics of infrared radiation. Optimising and integrating infrared into an asset management subsystem requires correct monitoring equipment selection and accurate data collection including; Optimum radiometer wavelength, background characterization, spatial and thermal resolution and emissivity. This paper concludes with an industrial atlas of infrared normal and abnormal images, which are a useful reference in determining the various conditions in medical diagnostics with the primary intention to identify early onset of problems as part of an optimised asset management system.
published_date 2020-08-28T04:12:30Z
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