IEEE Transactions on Industrial Electronics· 2014Q1· Review
A Review on Basic Data-Driven Approaches for Industrial Process Monitoring
- 1,675citations
- Q1SCImago
- 2014year
Short summary
This review surveys widely applied data-driven methodologies for industrial process monitoring and fault diagnosis, proposing a modified design framework for diverse operating conditions.
AI-generated from the title and abstract; the full text is not read.
Key points
- Data-driven methods are increasingly important for industrial process monitoring and fault diagnosis.
- Real operating conditions present significant challenges for basic data-driven methods.
- The paper surveys widely applied data-driven methodologies from an application perspective.
- A modified data-driven design framework is proposed as a reference for industrial use.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. IEEE Transactions on Industrial Electronics, 2014 · DOI ↗
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Field: Control and Systems Engineering
Control and Systems EngineeringEngineering