Paper Info
Title
Decision fusion for reliable fault classification in energy-intensive process industries
Abstract
Heavy industries such as oil & gas, steel & iron production, the cement industry, chemical processes, and pulp & paper mills are among the industries that contribute most to greenhouse gas emissions. The operation of processes in these industries requires massive amounts of energy, which is mainly generated through the consumption of natural resources, leading to the emission of harmful gases. These emissions can be reduced by improving the efficiency of process operations and with better management of abnormal events/faults through accurate fault classification. Managers and operators of these industries need intelligent fault classification strategies that provide accurate information and decisions on faulty situations management, thus helping day-to-day processes operate in safe, reliable and energy-efficient ways. This paper proposes a decision fusion approach that combines outputs produced by diversified machine learning fault classifiers, each with distinct pattern representations. The proposed approach mainly adopts the behavior knowledge space (BKS) method and updates the corresponding lookup table based on the F1-scores calculated for every single fault classifier based on historical data. The concept is based on the exploitation of the advantages of each fault classifier as a complementary information source to effectively classify faults and provide plant operators and engineers with unified, accurate and comprehensive decisions. The proposed approach is validated through two case studies in the pulp and paper industry. The first one is the pulp mill process benchmark using simulated data and the second case is on a reboiler in a Canadian thermomechanical pulp mill. The results obtained demonstrate that the accuracy of the proposed approach is higher than the accuracy of every single classifier and other comparable methods applied to such complex industrial processes. The results have helped the mill operators correctly identify the causes of abnormal events and have contributed to significant energy savings and reduction in Greenhouse Gas (GHG) emissions. (c) 2022 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2022
10.1016/j.compind.2022.103640
COMPUTERS IN INDUSTRY
Keywords
DocType
Volume
Decision fusion, Machine learning, Decision support systems, Energy-intensive industries, Fault Detection and Diagnosis (FDD), Causality analysis, Energy efficiency
Journal
138
ISSN
Citations 
PageRank 
0166-3615
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Ahmed Ragab1303.93
Hakim Ghezzaz200.34
Mouloud Amazouz300.34