IoT-enabled Condition Monitoring and Prognostics for Machine Tools in Production Environments

Authors

  • MD ASIF  Selection Grade Lecturer, Mechanical Engineering Department, Government Polytechnic, Kalaburagi, Karnataka, India.
  • Nijananda Reddy  Selection Grade Lecturer, Mechanical Engineering Department, Government Polytechnic, Raichur, Karnataka, India.

Keywords:

Internet of Things (IoT), Condition Monitoring, Prognostics and Health Management (PHM), Machine Tools, Predictive Maintenance, Production Environments, Machine Learning, Sensor Technology, Data Analytics, Industrial IoT (IIoT), Operational Efficiency.

Abstract

This paper investigates the application of Internet of Things (IoT) technology in the condition monitoring and prognostics of machine tools within production environments. The primary aim is to enhance predictive maintenance strategies, thereby reducing unscheduled downtime and extending the operational life of machine tools. Through a comprehensive literature review, we identify existing gaps in the application of IoT for industrial maintenance, including the need for robust prognostic models and real-time monitoring capabilities. We propose an IoT-enabled system architecture that integrates advanced sensors for real-time data collection, including vibration, temperature, and operational parameters. This study employs a mixed-methods approach, leveraging both statistical and machine learning algorithms, to analyze the collected data and develop a predictive model for machine tool failure. The model's performance was evaluated in a real-world production setting, focusing on its accuracy in predicting tool wear and potential failures. Our findings indicate that the implementation of an IoT-enabled condition monitoring and prognostic system significantly enhances the ability to predict and prevent machine tool failures, leading to reduced maintenance costs and improved production efficiency. The system demonstrated a notable improvement in predictive maintenance strategy, enabling proactive interventions that minimize downtime and extend the life of machine tools. This research contributes to the body of knowledge by providing a validated framework for the integration of IoT in machine tool monitoring and prognostics. It also outlines the challenges encountered during implementation and proposes directions for future research, particularly in the development of more sophisticated predictive models and the integration of diverse data sources. The implications of this study are significant for manufacturers seeking to leverage IoT technology to enhance their maintenance strategies and improve overall production efficiency.

References

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International Journal of Scientific Research in Science and Technology

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Published

2016-01-30

Issue

Volume 2, Issue 1, January-February-2016

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
MD ASIF, Nijananda Reddy, " IoT-enabled Condition Monitoring and Prognostics for Machine Tools in Production Environments, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 2, Issue 1, pp.263-272, January-February-2016.