Aug 2026· International Journal of Modern Science and Research Technology· Vol 4, pp. 120· 0 citations· 19 references
TL;DR
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 is introduced.
Abstract
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, ROCAUC, 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 laboratoryacquired fault data.
The paper proposes a deep learning-based framework for real-time fault detection in the networked structure of cyber-physical systems (CPS), using embedded edge devices. The suggested method combines multi-modal sensor data fusion with lightweight neural models and an adaptive feedback mechanism, enabling efficient on-device inference in dynamic situations. According to the experimental analysis of CPS benchmarks for detection accuracy, latency, and energy consumption, there is a +19.3 % improvement in detection accuracy, a -18.6 % decrease in latency, and a -13.1 % decrease in energy consumption relative to the baseline models. Moreover, there are system reliability gains of +21.8%, giving it resilience in the noisy and time-sensitive environment. Scalable deployment in industrial automation, smart grids, and autonomous systems is supported by the architecture, which has low computational overhead. These findings substantiate the claim that deep learning with edge computing can be significantly more responsive, flexible and energy-efficient for real-time CPS fault detection.
Mohammad Wasim Bhatt, R. R. Maaliw, Mohammed Wasim Bhatt· International Journal on Com...· 0 citations
This paper proposes a method of fault detection in analog circuits which involves various steps, including selection of benchmark circuits, dataset preparation, signal decomposition, model training, and performance analysis. The main aim of this work is to provide solid performance even in noisy environments. Monte Carlo analysis is used to generate a synthetic dataset with 200 runs per fault class by introducing component tolerances and realistic faults. A multi-stage pipeline is proposed; it begins with resampling the signals and normalizing them, and then noise is added at different levels: 5 dB, 10 dB and 20 dB. Feature fusion is performed by combining time-, frequency-, and statistical-domain features. Statistical-domain features are extracted by applying Variational Mode Decomposition (VMD) to split them into four IMF levels, followed by the application of Continuous Wavelet Transform (CWT) for time–frequency-domain analysis. Support Vector Machine (SVM), Random Forest, and Gradient Boosting are used as base-level classification models. A stacking ensemble model is developed which uses Random Forest, Gradient Boosting, and Extra Trees as base learners and Logistic Regression as the meta-learner.
Archana Dhamotharan, Balakumar Muniandi, Vennila Anandaraj Umapathy et al.· Journal of Sensor and Actuat...· 0 citations
Motor drive systems operating in embedded environments are frequently affected by noise, dynamic loading conditions, and electromagnetic interference, making timely fault diagnosis difficult. To improve diagnostic accuracy and real-time performance, this study proposes an intelligent fault diagnosis framework based on embedded multi-source data acquisition and feature fusion. High-precision sensors are employed to synchronously collect vibration and current signals, while improved wavelet packet decomposition and principal component analysis are combined to extract discriminative multi-dimensional fault features and eliminate redundant information. A lightweight convolutional neural network optimized for embedded deployment is then developed to perform low-latency fault classification and edge inference. Experimental results show that the proposed method achieves an average F1-score exceeding 97% under complex mixed-fault conditions, while maintaining an average detection latency of approximately 45 ms. The proposed framework demonstrates strong robustness and computational efficiency, providing practical support for intelligent industrial maintenance and offering reference solutions for embedded sensing, signal processing, and electromagnetic compatibility environments.
This paper proposes a knowledge-based input configuration to inform deep learning models for both electrical and mechanical fault diagnosis, rather than increasing model complexity, and confirms that, while conventional feature processing techniques perform well for electrical fault diagnosis, only the proposed FFT-informed input effectively captures both electrical and mechanical fault patterns.
Jingyi Yan, Hariram Arni, Bin Jou et al.· Measurement· 1 citation
Results indicate that artificial intelligence can significantly strengthen the resilience and automation of next-generation smart grid infrastructures.
T. Anvesh, Akshaya Chelpuri, Ambati Chandu· International Scientific Jou...· 0 citations
The article addresses the problem of a tonal signal binary detection in the presence of additive white Gaussian noise in real-time. A comparative analysis of classical methods (energy detector, quadrature matched filter) and neural network approaches (1D Convolutional Neural Network – 1D CNN, Multilayer Perceptron – MLP) is conducted during their hardware implementation on the STM32F407 microcontroller and the Artix-7 Field-Programmable Gate Array (FPGA). It is experimentally demonstrated that the 1D CNN architecture under low signal-to-noise ratio (SNR from –10 to –8 dB) conditions exhibits higher classification accuracy compared to classical methods and approaches the theoretical optimum. The impact of post-training 8-bit quantization on model size and accuracy is investigated. Practical hardware implementation metrics are provided: for the STM32 platform, the inference time is 2.1 ms with an average current consumption of 7 mA, while the FPGA implementation ensures a processing latency of less than 1 μs with absolute determinism. Practical recommendations for selecting a hardware platform based on power consumption, latency, and system flexibility requirements are formulated.
O. Vorgul, A. V. Onishchenko, G.O. Serbsky· Radiotekhnika· 0 citations
