Fuzzy Based Approach for Fault Detection and Diagnosis in Pneumatic Actuator in Cement Industry

Abstract

Fault detection and diagnosis is an important task with increasing attention in the academic and industrial fields, due to economical and safety related matters. The early detection of fault can help avoid system shutdown, breakdown and even catastrophe involving human fatalities and material damage. In fault detection, the discrepancies between system outputs and model outputs are called residuals, and are used to detect and diagnose faults. Computational intelligence techniques are being investigated as an extension to the traditional fault detection and diagnosis methods. This paper proposes a fuzzy based architecture for fault detection and diagnosis based on fuzzy classification approach. The real time data for pneumatic actuator has obtained from the cement industry under normal and abnormal operating conditions. In this paper the proposed fuzzy architecture is able to detect the thirteen numbers of possible faults in pneumatic actuator for cooler water spray system in cement industry, effectively when compared with Hazard and Operability (HAZOP) study.