Development of an Intelligent Fault Diagnosis Tool for iPhone Motherboards: Power Consumption Analysis Using Deep Learning

This study presents an intelligent microcontroller-based diagnostic tool and application designed to enhance fault detection accuracy and efficiency in iPhone motherboards, utilizing power consumption data and deep learning (DL) for real-time diagnostics. Integrating an RP2040 microcontroller and INA226 current sensor, the tool captures power patterns during boot-up, a method applicable across embedded systems and robotics for predictive fault analysis and maintenance. The tool, deployed in phone repair centers, has generated a comprehensive dataset of over 1,600 iPhone 6s devices with faults linked to 12 distinct power rails. Various deep learning models, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, were evaluated, with the LSTM achieving the highest accuracy (99%) and F1-score (0.997) for precise fault classification. This diagnostic application communicates with a central server, enabling a scalable and automated framework suitable for robotics and intelligent systems requiring power diagnostics. By introducing DL-based power consumption analysis, this study pioneers an approach with broad implications for intelligent maintenance in embedded and robotic systems. Our findings offer a foundation for faster, automated, and reliable diagnostics, potentially advancing fault management in robotic applications and other intelligent devices reliant on precise power monitoring and control.