Investigating the Use of Artificial Intelligence for Fault Detection in Electronic Circuits

Abstract

Many control systems and industrial applications rely heavily on analogue electrical circuits. Diagnosing problems with such circuits the old-fashioned manner can be erroneous and time consuming, which can have a detrimental impact on the industrial product. Also Keeping up with the ever-increasing sophistication of modern electronics has made testing these systems a formidable challenge. Due to the complexity and amount of individual parts on a PCB, traditional simulation-based methods cannot be used, and instead, artificial intelligence-based methods must be prioritization intriguing area of study that has the potential to improve the safety and reliability of electronic systems is the use of artificial intelligence (AI) to the task of detecting faults in electronic circuits. Real-time defect identification, diagnosis, and localization are made possible by AI-based solutions, which can assist prevent system breakdowns and boost performance. Fault detection in electronic circuits has been approached using a wide variety of artificial intelligence (AI) based methods, each with their own set of benefits and drawbacks. The availability of labelled training data, the generalizability of AI models, and the interpretation of AI-based fault detection outcomes are just a few of the obstacles that must be overcome. Despite these limitations, problem detection based on AI has many potentials uses in industries like transportation, energy, healthcare, and more. In this study, we survey the landscape of available AI-based fault detection systems for electronic circuits, discussing their merits and shortcomings, difficulties, and possible applications. This study details an inquiry into the viability of utilising an AI approach to failure prediction and early detection in power systems. A machine-learning detector has been created to keep an eye on specific parts of electricity grids and anticipate any problems before they occur.The AI failure prediction system can quickly anticipate a system failure.