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A systematic review of multivariate uncertainty quantification for engineering systems

Grenyer, A., Erkoyuncu, J. A., Zhao, Y. and Roy, R. ORCID: 0000-0001-5491-7437 (2021). A systematic review of multivariate uncertainty quantification for engineering systems. CIRP Journal of Manufacturing Science and Technology, 33, pp. 188-208. doi: 10.1016/j.cirpj.2021.03.004

Abstract

Engineering systems must function effectively whilst maintaining reliability in service. Predicting maintenance costs and asset availability raises varying degrees of uncertainty from multiple sources. Previous reviews in this domain have assessed cost uncertainty and estimation for the entire life cycle. This paper presents a systematic review to investigate existing methodologies and challenges in uncertainty quantification, aggregation and forecasting for modern engineering systems through their in-service life. Approaches to forecast uncertainty here are hindered chiefly by data quality of available data, experience and knowledge. A total of 107 papers were analysed to answer three research questions based on the scope, through which two core research gaps were identified. An integrated combination of identified approaches will enhance rigour in uncertainty assessment and forecasting. This review contributes a systematic identification and assessment of current practices in uncertainty quantification and scientific methodologies to quantify, aggregate and forecast quantitative and qualitative uncertainties to better understand their impact on cost and availability to aid decision making throughout the in-service phase.

Publication Type: Article
Additional Information: © 2021 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).
Publisher Keywords: Aggregation, Engineering systems, Forecasting, Multivariate, Uncertainty analysis, Uncertainty quantification
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Mathematics, Computer Science & Engineering
Date Deposited: 26 Apr 2021 09:12
URI: https://openaccess.city.ac.uk/id/eprint/25956
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