Industrial IoT Condition Monitoring using Wireless IoT Sensor

To detect problems with industrial machines, this study developed a rigorous methodology for predictive maintenance. Predictive maintenance is a developing area of study with the goals of extending the useful life of machinery and cutting down on unplanned downtime by using data gleaned through the Internet of Things (IoT) monitoring conducted via wireless sensors. The suggested framework will comprise data collection, processing, analyzing it using a machine learning strategy, and making a conclusion for predictive maintenance. The suggested model is implemented in a LabView simulation developed to track the health of a piece of industrial machinery with fluctuating vibration levels. This study demonstrated the efficacy of the suggested model for predictive maintenance by analyzing current, temperature, and vibration patterns in LabView. Overall, the research makes a valuable contribution by using a Machine Learning technique and proposing a systematic framework for failure detection of industrial equipment using IoT applications.