A systematic review of multivariate uncertainty quantification for engineering systems

被引:16
作者
Grenyer, Alex [1 ]
Erkoyuncu, John A. [1 ]
Zhao, Yifan [1 ]
Roy, Rajkumar [2 ]
机构
[1] Cranfield Univ, Through Life Engn Serv Ctr, Cranfield MK43 0AL, Beds, England
[2] City Univ London, Sch Math Comp Sci & Engn, London EC1V 0HB, England
基金
英国工程与自然科学研究理事会;
关键词
Aggregation; Engineering systems; Forecasting; Multivariate; Uncertainty analysis; Uncertainty quantification; ALTERNATIVE REPRESENTATIONS; GEOMETRIC QUANTIFICATION; UNKNOWN-UNKNOWNS; COST UNCERTAINTY; PERFORMANCE; INFORMATION; PROPAGATION; SIMULATION; DESIGN; SCHEME;
D O I
10.1016/j.cirpj.2021.03.004
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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. (C) 2021 The Author(s).
引用
收藏
页码:188 / 208
页数:21
相关论文
共 132 条
[1]  
Adasooriya Nirosha D., 2016, Case Studies in Structural Engineering, V6, P1, DOI 10.1016/j.csse.2016.04.002
[2]   Degradation study of heat exchangers [J].
Addepalli, Sri ;
Eiroa, David ;
Lieotrakool, Suphansa ;
Francois, Anne-Laure ;
Guisset, Juliette ;
Sanjaime, David ;
Kazarian, Michele ;
Duda, Julia ;
Roy, Rajkumar ;
Phillips, Paul .
PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON THROUGH-LIFE ENGINEERING SERVICES, 2015, 38 :137-142
[3]   A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization [J].
Ahmed, R. ;
Sreeram, V ;
Mishra, Y. ;
Arif, M. D. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2020, 124
[4]  
Ahmed T., 2016, Journal of Uncertainty Analysis and Applications, V4, P9, DOI [10.1186/s40467-016-0051-2, DOI 10.1186/S40467-016-0051-2]
[5]  
Amin M.S. R., 2015, Numerical Methods for Reliability and Safety Assessment, P179
[6]   Some Considerations on the Definition of Risk Based on Concepts of Systems Theory and Probability [J].
Andretta, Massimo .
RISK ANALYSIS, 2014, 34 (07) :1184-1195
[7]  
[Anonymous], 2007, INCOSE INT S, DOI DOI 10.1002/J.2334-5837.2007.TB02903.X
[8]  
[Anonymous], 2010, CAMBRIDGE DICT STAT
[9]   Work roll cooling system design optimisation in presence of uncertainty and constrains [J].
Azene, Y. T. ;
Roy, R. ;
Farrugia, D. ;
Onisa, C. ;
Mehnen, J. ;
Trautmann, H. .
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY, 2010, 2 (04) :290-298
[10]   Data Quality Assessment of the Uncertainty Analysis Applied to the Greenhouse Gas Emissions of a Dairy Cow System [J].
Baek, Chun-Youl ;
Park, Kyu-Hyun ;
Tahara, Kiyotaka ;
Chun, Yoon-Young .
SUSTAINABILITY, 2017, 9 (10)