Fault diagnosis of the polypropylene production process (UNIPOL PP) using ANFIS
Document Type
Article
Publication Date
1-1-2010
Abstract
The performance of a chemical process plant can gradually degrade due to deterioration of the process equipment and unpermitted deviation of the characteristic variables of the system. Hence, advanced supervision is required for early detection, isolation and correction of abnormal conditions. This work presents the use of an adaptive neurofuzzy inference system (ANFIS) for online fault diagnosis of a gas-phase polypropylene production process with emphasis on fast and accurate diagnosis, multiple fault identification and adaptability. The most influential inputs are selected from the raw measured data sets and fed to multiple ANFIS classifiers to identify faults occurring in the process, eliminating the requirement of a detailed process model. Simulation results illustrated that the proposed method effectively diagnosed different fault types and severities, and that it has a better performance compared to a conventional multivariate statistical approach based on principal component analysis (PCA). The proposed method is shown to be simple to apply, robust to measurement noise and able to rapidly discriminate between multiple faults occurring simultaneously. This method is applicable for plant-wide monitoring and can serve as an early warning system to identify process upsets that could threaten the process operation ahead of time.
Keywords
ANFIS, Fault diagnosis, Multiple faults, Plant-wide monitoring, Polypropylene production process, Abnormal conditions, Adaptive neuro-fuzzy inference system, ANFIS classifier, Chemical process plants, Early detection, Early Warning System, Fault types, Gasphase, Measured data, Measurement Noise, Multiple fault identification, Multivariate statistical approaches, On-line fault diagnosis, Process equipments, Process model, Process operation, Production proces, Simulation result, Chemical equipment, Monitoring, Multivariant analysis, Production engineering, Thermoplastics, Principal component analysis.
Divisions
fac_eng
Publication Title
ISA Trans
Volume
49
Issue
4
Publisher
ISA Trans
Additional Information
Lau, C K Heng, Y S Hussain, M A Mohamad Nor, M I eng 2010/07/30 06:00 ISA Trans. 2010 Oct;49(4):559-66. doi: 10.1016/j.isatra.2010.06.007. Epub 2010 Jul 27.