A Two-Step Fault Detection and Diagnosis Framework for Chemical Processes

  • Lau Chee Kong Department of Chemical Engineering, Faculty of Engineering. University of Malaya, 50603 Kuala Lumpur, Malaysia
  • Che Rosman Department of Chemical Engineering, Faculty of Engineering. University of Malaya, 50603 Kuala Lumpur, Malaysia
  • Che Hasan Department of Chemical Engineering, Faculty of Engineering. University of Malaya, 50603 Kuala Lumpur, Malaysia
  • Mohd Azlan Huzzain Department of Chemical Engineering, Faculty of Engineering. University of Malaya, 50603 Kuala Lumpur, Malaysia
Keywords: fault detection, fault diagnosis, boundary models, FIS, ANFIS

Abstract

An effective process monitoring system serves as an early warning system for influences affecting the chemical plant and helps plant operator to devise remedial actions to mitigate the adverse effects. However, the design of such system presents challenges such as complex cause-effect correlations, imprecise process model and novelty identifiability. In this work, a two-step fault detection and diagnosis framework is presented. This framework utilizes boundary models developed from mass and energy balances for each section of the chemical plant. The fault detection step consists of a fuzzy inference system (FIS) to analyze the balances and identify the faulty section if the balances deviate from the normal boundary. Then, multiple adaptive neuro-fuzzy inference system (ANFIS) classifiers are constructed to diagnose the exact root causes of bad performance. The combination of boundary models and FIS provides fault isolation of the faulty plant section even when novel faults have occurred. Utilization of multiple ANFIS classifiers reduces the complexity of the networks and improves the proficiency of the process monitoring system. The proposed scheme is applied on a model of a large scale industrial process.

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Published
2010-12-31
How to Cite
Kong, L. C., Rosman, C., Hasan, C., & Huzzain, M. A. (2010). A Two-Step Fault Detection and Diagnosis Framework for Chemical Processes. ASEAN Journal of Chemical Engineering, 10(2), 1-9. Retrieved from https://dev.journal.ugm.ac.id/v3/AJChE/article/view/8065
Issue
Vol 10 No 2 (2010)
Section
Articles

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