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Progress in Predictive Asset Maintenance Management

Daniel Rees Orcid Logo, Roderick Thomas Orcid Logo

International Journal of Condition Monitoring and Diagnostics Engineering Management, Volume: 24, Issue: 3, Pages: 39 - 45

Swansea University Authors: Daniel Rees Orcid Logo, 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 conditionmonitoring and predictive maintenance. The integration of this technology and associated communication technologies are improving operational aspec...

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Published in: International Journal of Condition Monitoring and Diagnostics Engineering Management
ISSN: 1363-7681
Published: COMADEM International ,UK 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa58036
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first_indexed 2021-09-23T11:05:32Z
last_indexed 2021-10-29T03:24:04Z
id cronfa58036
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spelling 2021-10-28T12:50:32.3558543 v2 58036 2021-09-23 Progress in Predictive Asset Maintenance Management daa6762111f9ebf62b9c2ec655512783 0000-0003-0372-6096 Daniel Rees Daniel Rees true false 891091891b6eee412668ae216f713312 0000-0002-2792-1251 Roderick Thomas Roderick Thomas true false 2021-09-23 BBU Optimised Asset Management in recent years has embraced a rapid diffusion of innovation and disruptive technology, especially with reference to conditionmonitoring and predictive maintenance. The integration of this technology and associated communication technologies are improving operational aspectsof industry, including predicting condition asset failure. Some assets in the future will be connected to the Internet of Things (IoT), Cloud Computing, BigData including virtual and augmented reality which will be the gateway to enhanced communication including self-diagnosing capabilities driven by expertsystems. Asset connectivity and predictive analytics will discern patterns and algorithms leading to optimised plant production, and enhanced energyefficiency. Machine learning models will indicate future operation on a real-time basis using big data libraries, tabular databases especially with referenceto condition monitoring. This paper reviews the role maintenance has in optimising asset operation. Journal Article International Journal of Condition Monitoring and Diagnostics Engineering Management 24 3 39 45 COMADEM International ,UK 1363-7681 Asset Management, Disruptive Technology, Condition Monitoring, Infrared Thermography, Reliability Based Maintenance. 22 9 2021 2021-09-22 COLLEGE NANME Business COLLEGE CODE BBU Swansea University SU College/Department paid the OA fee School of Management 2021-10-28T12:50:32.3558543 2021-09-23T11:55:45.1194364 Faculty of Humanities and Social Sciences School of Management - Business Management Daniel Rees 0000-0003-0372-6096 1 Roderick Thomas 0000-0002-2792-1251 2
title Progress in Predictive Asset Maintenance Management
spellingShingle Progress in Predictive Asset Maintenance Management
Daniel Rees
Roderick Thomas
title_short Progress in Predictive Asset Maintenance Management
title_full Progress in Predictive Asset Maintenance Management
title_fullStr Progress in Predictive Asset Maintenance Management
title_full_unstemmed Progress in Predictive Asset Maintenance Management
title_sort Progress in Predictive Asset Maintenance Management
author_id_str_mv daa6762111f9ebf62b9c2ec655512783
891091891b6eee412668ae216f713312
author_id_fullname_str_mv daa6762111f9ebf62b9c2ec655512783_***_Daniel Rees
891091891b6eee412668ae216f713312_***_Roderick Thomas
author Daniel Rees
Roderick Thomas
author2 Daniel Rees
Roderick Thomas
format Journal article
container_title International Journal of Condition Monitoring and Diagnostics Engineering Management
container_volume 24
container_issue 3
container_start_page 39
publishDate 2021
institution Swansea University
issn 1363-7681
publisher COMADEM International ,UK
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 0
active_str 0
description Optimised Asset Management in recent years has embraced a rapid diffusion of innovation and disruptive technology, especially with reference to conditionmonitoring and predictive maintenance. The integration of this technology and associated communication technologies are improving operational aspectsof industry, including predicting condition asset failure. Some assets in the future will be connected to the Internet of Things (IoT), Cloud Computing, BigData including virtual and augmented reality which will be the gateway to enhanced communication including self-diagnosing capabilities driven by expertsystems. Asset connectivity and predictive analytics will discern patterns and algorithms leading to optimised plant production, and enhanced energyefficiency. Machine learning models will indicate future operation on a real-time basis using big data libraries, tabular databases especially with referenceto condition monitoring. This paper reviews the role maintenance has in optimising asset operation.
published_date 2021-09-22T04:14:14Z
_version_ 1763753962555572224
score 11.012678

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