An Online Fault Diagnosis Strategy for Full Operating Cycles of Chemical Processes

CATEGORY: FAULT DIAGNOSIS
Ind. Eng. Chem. Res., Article ASAP, DOI: 10.1021/ie400660e, Publication Date (Web): July 19, 2013
An Online Fault Diagnosis Strategy for Full Operating Cycles of Chemical Processes
Jinsong Zhao †, Yidan Shu †, Jianfeng Zhu †, and Yiyang Dai *‡
daiyiyang1984@gmail.com
† State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, China
‡ CNPC Research Institute of Safety & Environment Technology
Abstract
Online fault diagnosis helps to ensure stability and safety in many chemical processes. Authors describe a lab-scale distillation process designed and built for a fault diagnosis study. The online fault diagnosis system (OFDS) was created using a distributed control system (DCS) system and a real-time database.
They used artificial neural networks (ANNs) for startup state judgment and for fault detection in the steady state. They employed the dynamic artificial immune system (DAIS) for fault detection in the startup phase and for fault identification in both the startup phase and the steady state. Results of case studies demonstrate that the system is efficient in online fault diagnosis of distillation processes during the full operating cycle, especially when the number of historical fault samples is limited. The self-learning ability of the methods ensures that the system can remember and diagnose new faults. The friendly interface of OFDS can show the current condition of the process to operators and get feedback from the operators for online learning.
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