Detection of Electronic Component Anomalies Through Data Mining

In modern electronics systems, predicting and preventing failures and malfunctions is crucial for ensuring reliability and reducing downtime. In this study, we present an approach for predicting electronics failure and malfunction by analyzing temperature and vibration data collected under various operational conditions. Our approach integrates data from multiple sensors monitoring temperature and vibration levels, providing a comprehensive view of system health. After setting the sensors, several data under different conditions were obtained. Through extensive experimentation and validation on real-world datasets, we demonstrate the effectiveness of our vibration and temperature data in anticipating electronics failures and malfunctions. Furthermore, we discuss the practical implications of our research, including potential applications for condition-based maintenance and proactive fault management in electronics systems. Overall, this work contributes to enhancing the reliability and performance of electronics systems in various applications, ranging from consumer electronics to industrial automation.