IoT Signal Classification using CNN over 5G for Industrial Fault Detection

Abstract

Rapid advancement of the 5G networks and the World Wide Web has made remarkable improvement in the concepts of real-time data gathering and processing for the industrial applications. Especially in terms of industrial monitoring and defect detection where IoT signal processing is very essential, reducing downtime during operations, most of the old-age systems depend very heavily on manual detection methods or scant data analytics that leave many breakdowns unidentified, increasing the costs of maintenance. Old technologies were literally far behind in terms of accuracy and real-time applications required in any modern industrial setting. In this research work proposes a real-time IoT signal-processing framework for machine fault detection based on CNNs. Low-latency data transmission is facilitated by 5G networks so that fast analysis and decision-making can be done. The classification model based on CNN offered an accuracy of 95.2%, while precision was 93.8% and recall was 94.5% with respect to a latency of only 35 ms. This work overcomes the limitations of existing systems by allowing continuous automated fault detection, thereby increasing the reliability of the system and minimizing downtime. The proposed method offers industrial sector applications enhanced predictive maintenance by utilizing intelligent AI and high-end 5G communication.