Creating a closed loop evironment for condition based maintenance plus (CMB+) and prognostics health management

Dussault, P.L.

doi:10.1109/autest.2007.4374237

“…An overview of the literature relative to TES and related topics forms the grounding for this research. The subsequent survey is informed by a review of the literature relative to CBM 1 , CBM 2 , CBM 3 , 15,[24][25][26] IVHM, [27][28][29][30] prognostic and diagnostic technology application, [31][32][33] MRO support and knowledge management systems 34 and PLM. 35,36 In seeking to offer a definition of TES, one sees no definitive descriptor emerging.…”

Section: Phase 1: Literature Review and Questionnaire Designmentioning

confidence: 99%

The adoption and use of diagnostic and prognostic technology within UK-based manufacturers

Grubić

¹

,

Redding

²

,

Baines

³

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part B:

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Manufacturing organisations seek ever more innovative approaches in order to maintain and improve their competitive position within the global market. One such initiative that is gaining significance is 'through-life engineering services'. These seek to adopt 'whole life' service support through the greater understanding of component and system performance driven by knowledge gained from maintenance, repair and overhaul activities. This research presents the findings of exploratory research based on a survey of UK manufacturers who provide through-life engineering services. The survey findings illustrate significant issues to be addressed within the field before the concept becomes widely accepted. These include a more proactive approach to maintenance activities based on real-time responses; standardisation of data content, structure, collection, storage and retrieval protocols in support of maintenance; the development of clear definitions, ontologies and a taxonomy of through-life engineering services in support of the service delivery system; lack of understanding of component and system performance due to the presence of 'No Fault Found' events that skew maintenance metrics and the increased use of radio-frequency identification technology in support of maintenance data acquisition.

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“…An overview of the literature relative to TES and related topics forms the grounding for this research. The subsequent survey is informed by a review of the literature relative to CBM 1 , CBM 2 , CBM 3 , 15,24–26 IVHM, 27–30 prognostic and diagnostic technology application, 31–33 MRO support and knowledge management systems 34 and PLM. 35,36…”

Section: Methodsmentioning

confidence: 99%

The adoption and use of through-life engineering services within UK manufacturing organisations

Redding

¹

,

Tiwari

²

,

Roy

³

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part B:

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Manufacturing organisations seek ever more innovative approaches in order to maintain and improve their competitive position within the global market. One such initiative that is gaining significance is 'through-life engineering services'. These seek to adopt 'whole life' service support through the greater understanding of component and system performance driven by knowledge gained from maintenance, repair and overhaul activities. This research presents the findings of exploratory research based on a survey of UK manufacturers who provide through-life engineering services. The survey findings illustrate significant issues to be addressed within the field before the concept becomes widely accepted. These include a more proactive approach to maintenance activities based on real-time responses; standardisation of data content, structure, collection, storage and retrieval protocols in support of maintenance; the development of clear definitions, ontologies and a taxonomy of through-life engineering services in support of the service delivery system; lack of understanding of component and system performance due to the presence of 'No Fault Found' events that skew maintenance metrics and the increased use of radio-frequency identification technology in support of maintenance data acquisition.

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“…This creates a robust system to optimize maintenance decision making in order to increase the reliability and expected lifetime of industrial systems. Industrial systems such as the automotive industry [38], [39], the U.S Department of Defence [40], the aerospace and aviation industries [41], [42], and manufacturing systems [43] have recently integrated PHM with success. PHM consists of seven steps initially defined from CBM [44].…”

Section: A Prognostics and Health Managementmentioning

confidence: 99%

A Comprehensive Survey of Prognostics and Health Management Based on Deep Learning for Autonomous Ships

Ellefsen

¹

,

Asoy

²

,

Ushakov

³

et al. 2019

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The maritime industry widely expects to have autonomous and semi-autonomous ships (autoships) in the near future. In order to operate and maintain complex and integrated systems in a safe, efficient and cost-beneficial manner, autoships will require intelligent Prognostics and Health Management (PHM) systems. Deep learning (DL) is a potential area for this development, as it is rapidly finding applications in a variety of domains, including self-driving cars, smartphones, vision systems, and more recently in PHM applications. This paper introduces and reviews four well-established DL techniques recently applied to various practical PHM problems. The purpose is to support creativity and provide inspiration towards PHM based on DL (PHMDL) in autoships and the maritime industry. This paper discusses benefits, challenges, suggestions, existing problems, and future research opportunities with respect to this significant new technology. Index Terms-Autonomous ships, deep learning, maritime industry, prognostics and health management.

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Creating a closed loop evironment for condition based maintenance plus (CMB+) and prognostics health management

Cited by 3 publications

References 7 publications

The adoption and use of diagnostic and prognostic technology within UK-based manufacturers

The adoption and use of diagnostic and prognostic technology within UK-based manufacturers

The adoption and use of through-life engineering services within UK manufacturing organisations

A Comprehensive Survey of Prognostics and Health Management Based on Deep Learning for Autonomous Ships

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