The escalating intricacy of very-large-scale integration (VLSI) circuits and embedded computer systems has rendered swift and dependable fault detection a crucial necessity for reliable electronic systems. Traditional testing and monitoring methodologies typically rely on manually crafted signal descriptors, established thresholds, or resource-intensive diagnostic techniques, which may prove inadequate when fault signatures are faint, noisy, non-stationary, or dispersed across various operational parameters. This manuscript introduces a real-time fault detection framework utilising deep learning, which integrates intelligent signal preprocessing, multi-domain feature representation, temporal deep learning, and efficient inference for monitoring VLSI and embedded systems. The suggested methodology acquires electrical and operational data including voltage, current, clock performance, temperature, power consumption metrics, timing discrepancies, and chosen telemetry from embedded systems. A signal-analysis layer executes normalisation, denoising, segmentation, and time-frequency transformation prior to a hybrid neural architecture acquiring distinguishing fault representations. The framework is intended to differentiate between standard operation and various fault circumstances relating to timing, power, thermal issues, signal integrity, transients, and hardware, while concurrently assessing fault confidence and severity. An interpretability component can be integrated to discern the signal periods and attributes that most significantly affect each diagnostic choice. The experimental protocol assesses accuracy, precision, recall, F1-score, ROC AUC, false-positive rate, inference latency, and throughput, alongside ablation and cross-condition evaluations. The suggested approach aims to establish a replicable connection between profound signal intelligence and instantaneous electronic system analysis. The document outlines the mathematical foundation, experimental methodology, comparative assessment structure, and implementation factors necessary for validation using benchmark, simulation-derived, or laboratory acquired fault data.

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