An approach to improve asset maintenance and management priorities using machine learning techniques

Anu Hanish Nithin, Michael Hobbs, Srinivas Sriramula* (Corresponding Author), Somayajulu Sripada

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Numerous services are available today to develop an optimised asset management solution to enhance asset operations by improving the system availability, decreasing down-time and operation and maintenance costs. Three cases of engineering problems are explored in this paper, with data-driven machine learning solutions proposed for these problems. The first case refers to the labour-intensive nature of criticality analysis which are used in asset management to prioritise assets. A machine learning solution is proposed by the development of a trained criticality analysis model, with a classification error of 12.35%, which could help in a better prediction of the end result by automating the process i.e., training the model. The second case looks at an application of machine learning on asset health prediction by analysing failure patterns and parameters for a machine. The model was evaluated with an error loss of 0.0024. The third case looks at an integration of the priorities related to asset maintenance and management through the development of a text classification machine learning service selector (landscape) tool and explores improvising the end-user selection of the services based on their challenges and perceived pain-points related to asset management. The model was evaluated with an accuracy of 84%.
Original languageEnglish
Pages (from-to)151-169
Number of pages19
JournalSafety and Reliability
Issue number3
Publication statusPublished - 23 Nov 2022

Bibliographical note

Open Access via T&F Agreement
Funding: This research project was funded and supported by Innovate UK and Optimal (Optima Asset Maintenance Solutions Ltd.) as part of the Knowledge Transfer Partnerships Scheme.


  • asset management
  • machine learning
  • criticality analysis
  • predictive maintenance
  • reliability engineering
  • deep learning


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