Diagnosis of Frequency Response Analog Circuits using HHO-SVM

Abstract

Monitoring the system, recognising when a fault has occurred, identifying the kind of defect and where it is located are all aspects of fault detection and isolation. To assess whether a problem has arisen inside a certain channel or region of operation, fault detection is used. For many technological processes in the creation of effective and safe advanced supervision systems, fault detection and diagnosis have grown in significance. This article's main goal is to increase the accuracy of faults detection in frequency response analogue circuits and execution of work needs to be speed up. For this purpose, two optimization techniques are used. One is grey wolf optimization (GWO) for the process of feature extraction and secondly Harris Hawk optimization (HHO) as classifier optimizer.   the features and optimize the classifier. The Sallen key circuit (SKC) are utilized for processing the input data. The filters like low pass, high pass and bandpass are designed based on SKC and optimized using GWO. Finally, the optimized features obtained from different circuits are fed to support vector machine classifier to identify the fault accuracy in the circuit. The SVM classifier is optimized using HHO to achieve best accurate output. The suggested technique with a low-dimensional feature optimisation and optimised classifier performed better than the prior methods according to simulation findings, and computing time was also greatly minimised.