A PSO-trained Adaptive Neuro-fuzzy Inference System for Fault Classification

When a fault occurs during an industrial inspection, workmen have to manually find the location and type of the fault in order to remove it. It is often difficult to accurately find the location and type of fault. Hence, development of an offline intelligent fault diagnosis system for process control industry is of great importance since successful detection of fault is a precursor to fault isolation using corrective actions. This paper presents a novel hybrid Particle Swarm Optimization (PSO) and Subtractive Clustering (SC) based Neuro-Fuzzy Inference System (ANFIS) designed for fault detection. The proposed model uses the PSO algorithm to find optimal parameters for (SC) based ANFIS training. The developed PSO-SC-ANFIS scheme provides critical information about the presence or absence of a fault. The proposed scheme is evaluated on a laboratory scale benchmark two-tank process. Leakage fault is detected and results are presented at the end of the paper showing successful diagnosis of most incipient faults when subjected to a fresh set of data.