Deep Learning Decision Support for Sustainable Asset Management

Marianne Cherrington; Zhongyu (joan) Lu; Qiang Xu; David Airehrour; Samaneh Madanian; Andrea Dyrkacz

doi:10.1007/978-3-030-57745-2_45

Deep Learning Decision Support for Sustainable Asset Management

Marianne Cherrington, Zhongyu (joan) Lu, Qiang Xu, David Airehrour, Samaneh Madanian, Andrea Dyrkacz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

Abstract

Sustainable decisions can be thwarted by a plethora of conflicting influences and information, yet the need to prioritise sustainability in management has never been greater. In an era of high- and ultra-high dimensional data, deep learning models offer the scalability required to extract good representations of significant features from raw data. With automatic learning at several levels of abstraction, deep learning can support sustainable asset management by learning complex functions mapping of systems. By processing data directly from input to output, clear and concise information can support visionary asset management whilst exposing hidden insights, detecting anomalies or predicting future states. This research will look at the necessity of applying deep learning in sustainable asset management and reveal some of the challenges that exist.

Original language	English
Title of host publication	Advances in Asset Management and Condition Monitoring
Subtitle of host publication	COMADEM 2019
Editors	Andrew Ball, Len Gelman, B. K. N. Rao
Place of Publication	Cham
Publisher	Springer Nature Switzerland AG
Chapter	45
Pages	537-547
Number of pages	11
Volume	166
Edition	1st
ISBN (Electronic)	9783030577452
ISBN (Print)	9783030577445
DOIs	https://doi.org/10.1007/978-3-030-57745-2_45
Publication status	Published - 28 Aug 2020
Event	32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference - University of Huddersfield, Huddersfield, United Kingdom Duration: 3 Sep 2019 → 5 Sep 2019 Conference number: 32 http://www.comadem2019.com/ (Link to Conference Website)

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	166
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference
Abbreviated title	COMADEM 2019
Country/Territory	United Kingdom
City	Huddersfield
Period	3/09/19 → 5/09/19
Internet address	http://www.comadem2019.com/ (Link to Conference Website)

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10.1007/978-3-030-57745-2_45Licence: Unspecified

Cite this

Cherrington, M., Lu, Z., Xu, Q., Airehrour, D., Madanian, S., & Dyrkacz, A. (2020). Deep Learning Decision Support for Sustainable Asset Management. In A. Ball, L. Gelman, & B. K. N. Rao (Eds.), Advances in Asset Management and Condition Monitoring: COMADEM 2019 (1st ed., Vol. 166, pp. 537-547). (Smart Innovation, Systems and Technologies; Vol. 166). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-57745-2_45

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title = "Deep Learning Decision Support for Sustainable Asset Management",

abstract = "Sustainable decisions can be thwarted by a plethora of conflicting influences and information, yet the need to prioritise sustainability in management has never been greater. In an era of high- and ultra-high dimensional data, deep learning models offer the scalability required to extract good representations of significant features from raw data. With automatic learning at several levels of abstraction, deep learning can support sustainable asset management by learning complex functions mapping of systems. By processing data directly from input to output, clear and concise information can support visionary asset management whilst exposing hidden insights, detecting anomalies or predicting future states. This research will look at the necessity of applying deep learning in sustainable asset management and reveal some of the challenges that exist.",

keywords = "Deep learning, Sustainable asset management, Feature selection, Feature extraction, Decision support, ESG ratings",

author = "Marianne Cherrington and Lu, {Zhongyu (joan)} and Qiang Xu and David Airehrour and Samaneh Madanian and Andrea Dyrkacz",

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Cherrington, M, Lu, Z, Xu, Q, Airehrour, D, Madanian, S & Dyrkacz, A 2020, Deep Learning Decision Support for Sustainable Asset Management. in A Ball, L Gelman & BKN Rao (eds), Advances in Asset Management and Condition Monitoring: COMADEM 2019. 1st edn, vol. 166, Smart Innovation, Systems and Technologies, vol. 166, Springer Nature Switzerland AG, Cham, pp. 537-547, 32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference, Huddersfield, United Kingdom, 3/09/19. https://doi.org/10.1007/978-3-030-57745-2_45

Deep Learning Decision Support for Sustainable Asset Management. / Cherrington, Marianne; Lu, Zhongyu (joan); Xu, Qiang et al.

Advances in Asset Management and Condition Monitoring: COMADEM 2019. ed. / Andrew Ball; Len Gelman; B. K. N. Rao. Vol. 166 1st. ed. Cham : Springer Nature Switzerland AG, 2020. p. 537-547 (Smart Innovation, Systems and Technologies; Vol. 166).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lu, Zhongyu (joan)

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AU - Airehrour, David

AU - Madanian, Samaneh

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N1 - Conference code: 32

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AB - Sustainable decisions can be thwarted by a plethora of conflicting influences and information, yet the need to prioritise sustainability in management has never been greater. In an era of high- and ultra-high dimensional data, deep learning models offer the scalability required to extract good representations of significant features from raw data. With automatic learning at several levels of abstraction, deep learning can support sustainable asset management by learning complex functions mapping of systems. By processing data directly from input to output, clear and concise information can support visionary asset management whilst exposing hidden insights, detecting anomalies or predicting future states. This research will look at the necessity of applying deep learning in sustainable asset management and reveal some of the challenges that exist.

KW - Deep learning

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KW - Feature selection

KW - Feature extraction

KW - Decision support

KW - ESG ratings

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Cherrington M, Lu Z, Xu Q, Airehrour D, Madanian S, Dyrkacz A. Deep Learning Decision Support for Sustainable Asset Management. In Ball A, Gelman L, Rao BKN, editors, Advances in Asset Management and Condition Monitoring: COMADEM 2019. 1st ed. Vol. 166. Cham: Springer Nature Switzerland AG. 2020. p. 537-547. (Smart Innovation, Systems and Technologies). Epub 2020 Aug 28. doi: 10.1007/978-3-030-57745-2_45

Deep Learning Decision Support for Sustainable Asset Management

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