Model-based fault detection and diagnosis of complex chemical processes: A case study of the Tennessee Eastman process

Abstract

Fault detection and diagnosis for industrial systems has been an important field of research during the past years. Among these systems, the Tennessee Eastman process is extensively used as a realistic benchmark to test and compare different fault detection and diagnosis strategies. In this context, data-driven approach has been widely applied for fault detection and diagnosis of the Tennessee Eastman process, by exploiting the massive amount of available measurement data. However, only few published works had attempted to deal with the dynamic behavior of the whole system including the mixing zone, circulating pumps, the reactor, the separator, the stripper, and so on, because of the difficulty of modeling physical phenomena that may occur in such complex system. In this article, an accurate model of the Tennessee Eastman process, properly tailored for fault detection and diagnosis purposes, is provided. This model shows better fault detection and diagnosis performances than all the others proposed in the literature and gives better or comparable results with the data-driven approaches. This work uses the bond graph methodology to systematically develop computational and graphical model. This methodology provides a physical understanding of the system and a description of its dynamic behavior. The bond graph model is then used for monitoring purposes by generating formal fault indicators, called residuals, and algorithms for fault detection and diagnosis. Hence, abnormal situations are detected by supervising the residuals' evolution and faults are isolated using the nature of the violated residuals. Therefore, the dynamic model of the Tennessee Eastman process can now be used as a basis to achieve accurately different analysis through the causal and structural features of the bond graph tool.

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“…[1][2][3][4] Data-driven fault diagnosis method has become a promising tool in engineering applications since no accurate physical model and information of correct expert knowledge are required. [5][6][7][8] Deep learning is an efficient data feature representation tool. It can be applied to data-driven fault diagnosis.…”

Application of Exergy-Based Fault Detection in a Gas-To-Liquids Process Plant

“…Although not impossible, the derivation of analytic models for PCI plants is considered to be challenging. An example of the complexity associated with the derivation of analytic models is provided in [ 6 ] for the Tennessee Eastman Process (TEP). However, in a review series, Gao et al [ 7 , 8 ] introduced the so-called signal-based diagnosis which makes use of measurement signals rather than analytic models of the plant.…”