Real-time Condition Monitoring of Industrial Machines using IoT and Mechanical Engineering Techniques

Abstract

In this paper, we offer a unique approach to industrial machine monitoring systems that integrates mechanical engineering and IoT technologies. The proposed system incorporates a number of sensors that collect real-time data on the machine's operational state, which is then evaluated using machine learning algorithms to identify probable errors before they occur. The system will send out early warning signals and recommendations to maintenance technicians, allowing them to take appropriate action before a failure occurs. As a result, machine dependability is improved, maintenance costs are decreased, and efficiency is increased. On an industrial machine, the proposed system was tested and demonstrated great accuracy in detecting possible faults.

The system's ability to detect possible failures was further tested in a case study in a manufacturing plant, where it was discovered to drastically reduce machine downtime and maintenance costs.

This work advances mechanical engineering and IoT by providing an innovative approach to industrial machine monitoring systems that integrates real-time data collection and analysis with machine learning algorithms. The proposed technology outperforms existing maintenance methods and is simple to implement in industrial settings.

Overall, this work illustrates the usefulness of the suggested system in improving machine uptime, lowering maintenance costs, and increasing efficiency, making it a significant resource for industrial machine maintenance researchers and practitioners.