Model based fault detection diagnosis and classification in shell and tube heat exchanger using intelligent techniques

Abstract

A method for Model-Based Approach for Fault Detection and Diagnosis has been put forth in this research. The purpose of this study is to build a heat exchanger model and to locate, categorize faults in the heat exchanger system. One of the known methods for fault control in industrial processes is fault detection and diagnosis (FDD). Both linear and non-linear systems can benefit from the FDD technique; this work describes fault identification and diagnosis for the non-linear system, the heat exchanger. A realistic mathematical model of the heat exchanger with its set of inputs and outputs has therefore been constructed, as the fault detection of the heat exchanger is related to the mathematical model for its operation. Once the mathematical model has been realized using the formulated equation, the process passes on to fault detection. Residual, which defines the failure symptoms of the system, is used to detect faults. In order to prevent failures and defective circumstances from occurring in existing systems, observer design is employed as one of the most powerful and reliable approaches residual generation. Fault diagnosis is carried out using a Fuzzy logic controller (FLC), which is designed based on the residuals obtained, and fault classification is made using the FLC via a fixed threshold. The paper's primary goals are to reduce errors and enhance non-linear systems' overall performance in industrial processes. According to the findings of the simulation, errors in sensors, processes, and actuators can be classified according to a set threshold, and they can be controlled using a traditional PID controller.