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Rama Fahrizal Kusuma
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Engineering

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A Systematic Literature Review on Machine Learning Techniques for Enhancing Embedded Hardware Reliability Desy Natalia; Cahya Renita Pulse; Rizal Ramadhan; Rama Fahrizal Kusuma; Rizky Ajie Aprilianto; Feddy Setio Pribadi
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 19 No. 3 (2025)
Publisher : Faculty of Engineering, Universitas Brawijaya

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Abstract

Embedded systems (ES) have played a vital role in industrial automation and critical infrastructure, but their reliability has often been compromised by hardware faults, leading to downtime and safety concerns. Traditional threshold-based fault detection methods have frequently failed to adapt to dynamic environments and have struggled to identify early-stage failures. This study reviewed the effectiveness of artificial intelligence (AI), specifically machine learning (ML) models, for fault detection in ES. A systematic review methodology was employed to analyze the diagnostic performance of several deep learning (DL) architectures, including hybrid convolutional neural network-long short-term memory (CNN-LSTM) models, when implemented on resource-constrained edge devices. The results showed that optimized AI models achieved higher diagnostic accuracy and earlier fault identification compared to conventional approaches. Furthermore, these models enabled real-time, energy-efficient operation on platforms such as Raspberry Pi and ESP32 microcontrollers. It was concluded that AI-driven solutions significantly enhanced predictive maintenance and operational reliability in embedded system applications, demonstrating their transformative potential for future industrial systems.
Co-Authors Cahya Renita Pulse Desy Natalia Feddy Setio Pribadi Rizal Ramadhan Rizky Ajie Aprilianto