{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T04:12:01Z","timestamp":1751429521393,"version":"3.41.0"},"reference-count":0,"publisher":"Advances in Artificial Intelligence and Machine Learning","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAIML"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.54364\/aaiml.2025.52215","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T12:00:52Z","timestamp":1751371252000},"page":"3784-3808","source":"Crossref","is-referenced-by-count":0,"title":["Development of an Intelligent Fault Diagnosis Tool for iPhone Motherboards: Power Consumption Analysis Using Deep Learning"],"prefix":"10.54364","volume":"05","author":[{"family":"P.D.K. Madhubhashana","sequence":"first","affiliation":[]},{"family":"H.D.N.V. Jayasekara","sequence":"additional","affiliation":[]},{"family":"G.D.G.N. Jayawardena","sequence":"additional","affiliation":[]},{"family":"B.N.S. Lankasena","sequence":"additional","affiliation":[]},{"family":"B.M. Seneviratne","sequence":"additional","affiliation":[]}],"member":"32807","published-online":{"date-parts":[[2025]]},"container-title":["Advances in Artificial Intelligence and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/940852215.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T12:00:56Z","timestamp":1751371256000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.oajaiml.com\/uploads\/archivepdf\/940852215.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":0,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.54364\/aaiml.2025.52215","relation":{},"ISSN":["2582-9793"],"issn-type":[{"value":"2582-9793","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}