AI-Powered Early Fault Detection System in Electronic Circuits

Abstract

Technical failures and problem may affect electric power systems and their essential industrial components, which in turn may affect the quality of electricity grid. Despite the significant developments in the plans of these systems, they may still face unexpected risks that may cause operational failures and decrease in efficiency. These failures may be from different sources which include thermal and systemic modifications during operation. It is essential to consider these malfunctions as the entire operating system may exacerbate the risks and create additional disruption and possibly add to the significant sudden losses. Thus, is is vital to enhance existing models in order to detect and classify failures and malfunctions and to place emphasis on these potential risks before they take place. This is very necessary to maintain the electrical systems to reduce and minimize the damage and support the total consistency of the system. This model will enable both earlier detection and accurate categorization to minimize the losses and deficiencies within the operating system and enhance the efficacy of automated and rapid procedure in the electronic circuits. The study may present a unified and structurally complete system that is capable oof analyzing the indicators obtained from the sound pumps and comparators, which attempt to align patterns to determine the disturbances and failures which affect power-related electrical mechanisms. It may utilize neural networks like the ANNs and the KNN algorithm, and employ the distinguishing LSTM model for process detection and error determination with unique efficacy and precision. The model is connected with automatic self-configured and automatic technologies to improve the operational capacity of the system and to improve its sustainability during and immediately after the identification of the fault, thereby enhancing the performance efficacy and consistency in the industrial motors fixed in the cells. The main motive of this study is the ability of the electronic system’s efficacy to identify the faults and predict failures that aim to anticipate malfunctions and provide immediate instant diagnosis before they take place. It also examines the system's ability to mitigate failures resulting from sudden and dangerous developments in critical systems. Therefore, it is necessary to improve and develop models capable of identifying patterns and classifying faults, and to attempt to link immediate service mechanisms with the system's capacity to reduce losses and improve the overall performance of the critical system, while ensuring continuous improvement and electrical circuit stability. This research addresses a gap in previous studies, which generally focused on fault detection in critical systems but failed to integrate immediate and self-remediation techniques within a comprehensive and balanced structural framework. It failed to implement a model on a real platform such as FPGA, in addition to the fact that it did not cover many tests in the event of branching and branching faults within the system, and real tests that involve pumps and comparisons of electronic circuits. Therefore, this study covers the research gap that helps in improving a balanced and consistent pattern that integrates artificial intelligence and automatic self-configuration mechanisms with real tests on FPGA, which helps in identifying and predicting faults and improving the accuracy and reliability of the system in a harmonious way.