IoT based condition monitoring of rotating machinery and predictive maintenance

Nazir Ali, Mohamad Nazmeer (2022) IoT based condition monitoring of rotating machinery and predictive maintenance. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

OEE is Overall Equipment Effectiveness, a hierarchy that measures the performance of a machine for enhanced productivity. The evolution of industry 4.0 is providing immense possibilities to monitor factory equipment like never before. OEE is a powerful tool that helps perform diagnostics as well as manages the production units in different industries. Internet of Things (IoT) technology is helping manufacturing agencies to improve their OEE evaluation with a detailed understanding of equipment performance through instrumentation and analytics. Therefore, the current project proposes an IoT platform for real-time monitoring of factory equipment, such as rotating machinery. The accelerometer sensor inside an Android-based smartphone collects vibration data from rotating machinery. The data is then published to the public MQTT broker via the smartphone's developed application. The application was created with MIT App Inventor, an open-source developer tool. The published data is then subscribed to through Node-RED and visualised in a series of dashboards. A real-time state of the rotating machinery is realised using the proposed system. Furthermore, the time domain data are transformed into the frequency domain in order to validate the collected data from the developed application. The frequencies from the machine are compared to those calculated from the frequency domain using the Fast Fourier Transform (FFT) method. The results showed that both frequencies were in good agreement, proving that the developed application was capable of sensing the correct data. The current project also includes the development of an early warning system as part of a predictive maintenance framework. The system is built with Node-RED as an IoT platform to notify the user of the machine's status via email. The results demonstrated that the developed early warning system could notify the user when the trigger condition is met. Finally, when combined with predictive maintenance, IoT technology has the potential to detect equipment failures in advance. With the introduction of Industry 4.0 in the manufacturing sector, facilities are eager to use IoT technology to gain better insights into operations.

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