Publication: Condition based monitoring (CBM) of a vibrating machinery using IoT with cloud data analysis
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Date
2024-07-12
Authors
Muhammad Iqbal Amin bin Ali
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Abstract
Condition-Based Monitoring (CBM) for vibrating machinery is essential to ensure operational efficiency, reduce downtime, and prevent unexpected failures. This thesis explores the integration of Internet of Things (IoT) technology with cloud-based data analytics to develop an advanced CBM system for vibrating machinery. By leveraging IoT sensors, real-time vibration data is collected from the machinery and transmitted to the cloud for in-depth analysis. This study utilizes Experimental Modal Analysis (EMA) and Operational Deflection Shapes (ODS) techniques to obtain critical vibration characteristics such as natural frequencies, damping ratios, and mode shapes. These parameters are crucial for diagnosing the health of machinery components and predicting potential issues.
The cloud-based platform enables advanced data processing and storage, facilitating the implementation of machine learning algorithms for anomaly detection and predictive maintenance. The integration of IoT and cloud technology ensures continuous monitoring and provides actionable insights through user-friendly dashboards and alerts. This approach enhances the reliability and performance of vibrating machinery by enabling timely maintenance interventions based on precise and comprehensive data analysis. The study results demonstrate the feasibility and effectiveness of the proposed CBM system, with significant improvements in fault detection accuracy and maintenance scheduling. This thesis also identifies areas for further research, such as the computation of Dynamic Vibration Absorbers (DVA) and improving the data analysis process in IoT applications. The developed system represents a significant advancement in industrial maintenance, offering scalable and robust solutions for real-time monitoring and analysis of vibrating machinery.