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A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

IEEE Transactions on Industrial Electronics · 2014 · Vol. 61(11) · pp. 6418–6428
Shen YinSteven X. DingXiaochen XieHao Luo

Abstract

Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

Fault Detection and Control SystemsMineral Processing and GrindingAdvanced Control Systems OptimizationProcess (computing)Computer scienceSketchReliability (semiconductor)Condition monitoringData-drivenTask (project management)Fault detection and isolationScale (ratio)Work in process

Funding

  • National Natural Science Foundation of China
Citations
1,648
FWCI
192.03
field-weighted impact
References
93
Percentile
100%
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Citations per year
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References
From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis
IEEE Transactions on Industrial Informatics · 2013 · 710 citations
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