Advances in Data-Driven Monitoring Methods for Complex Process

Abstract:

In modern industrial processes, effective performance monitoring and quality prediction are the key to ensure plant safety and enhance product quality. The research significance and background of process monitoring and fault diagnosis technologies are described and the current advances in data-based process monitoring methods are summed up in this paper. Then the multivariate statistical process control (MSPC) methods for process with single constraint, especially for single non-Gaussian process or nonlinear process are elaborated. As real industrial process data often show strong non-Gaussian and dynamic behaviors, study on monitoring technologies for dynamic non-Gaussian process is of great importance. Finally, some challenges such as non-Gaussian and dynamic process, fault detection and diagnosis as well as new MSPC methods are indicated.

• Periodical: Applied Mechanics and Materials (Volumes 423-426)
• Pages: 2448-2451
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.423-426.2448
• Online since: September 2013
• Authors: Ru Qing Chen*
• Keywords: Data-Driven Technique, Dynamic and Non-Gaussian Process, Multivariate Statistical Process Control, Process Monitoring and Fault Diagnosis

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