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

Abstract

Online fault diagnosis is one of the most important methods to ensure stability and safety in many chemical processes. In this work, a lab-scale distillation process is designed and built for fault diagnosis study, and the online fault diagnosis system (OFDS) is developed with a distributed control system (DCS) system and a real-time database. Artificial neural networks (ANNs) are used for startup state judgment and for fault detection in the steady state, while the dynamic artificial immune system (DAIS) is used for fault detection in the startup phase and for fault identification in both the startup phase and the steady state. The results of case studies clearly illustrate that the developed 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, and the friendly interface of OFDS can show the current condition of the process to operators and get feedback from the operators for online learning.