Manuscript Number : IJSRST1611216

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

Authors(2) :-MD ASIF, Nijananda Reddy

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.

Authors and Affiliations

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.

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.

  1. Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., & Liao, H. (2006). Intelligent Prognostics Tools and E-Maintenance. Computers in Industry, 57(6), 476-489.
  2.  Pecht, M., & Jaai, R. (2010). A Prognostics and Health Management Roadmap for Information and Electronics-Rich Systems. Microelectronics Reliability, 50(3), 317-323.
  3.  Hashemian, H.M., & Bean, W.C. (2011). State-of-the-Art Predictive Maintenance Techniques. IEEE Transactions on Instrumentation and Measurement, 60(10), 3480-3492.
  4.  Jardine, A.K.S., Lin, D., & Banjevic, D. (2006). A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510. 
  5. Lu, C., & Meeker, W.Q. (1993). Using Degradation Measures to Estimate a Time-to-Failure Distribution. Technometrics, 35(2), 161-174. 
  6. Mobley, R.K. (2002). An Introduction to Predictive Maintenance. Elsevier.
  7.  Zio, E. (2012). Prognostics and Health Management of Industrial Equipment. International Journal of Performability Engineering, 8(1), 79-94.
  8. Kothamasu, R., Huang, S.H., & VerDuin, W.H. (2006). System Health Monitoring and Prognostics—A Review of Current Paradigms and Practices. International Journal of Advanced Manufacturing Technology, 28(9-10), 1012-1024.
  9.  Sikorska, J.Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic Modelling Options for Remaining Useful Life Estimation by Industry. Mechanical Systems and Signal Processing, 25(5), 1803-1836.
  10.  Atamuradov, V., Medjaher, K., Camci, F., Zerhouni, N., & Dersin, P. (2010). Prognostics and Health Management for Maintenance Practitioners - Review, Implementation and Tools Evaluation. International Journal of Prognostics and Health Management, 3(4), 1-31.
  11.  Vachtsevanos, G., Lewis, F.L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems.
  12. John Wiley & Sons. Wang, W. (2002). Early Detection of Machine Tool Wear Using Wavelet Packet Transform and Probabilistic Neural Network. Journal of Manufacturing Science and Engineering, 124(4), 865-870.
  13.  Lin, D.T., & Yang, B.D. (2006). The Application of Genetic Algorithms to Parameter Estimation in Lead-Acid Battery Prognostics. Journal of Power Sources, 159(2), 1240-1249.
  14.  Schwabacher, M. (2005). A Survey of Data-Driven Prognostics. AIAA Infotech@Aerospace 2005, 1-5. 
  15. Goebel, K., Saha, B., & Saxena, A. (2008). A Comparison of Three Data-Driven Techniques for Prognostics. 62nd Meeting of the Society for Machinery Failure Prevention Technology, 119-131.
  16. Tsui, K.L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2013). Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators. IEEE Access, 1, 596-604. 
  17. Lee, J. (2001). A Framework for Web-Enabled E-Maintenance Systems. International Journal of Plant Engineering and Management, 6(2), 55-64.
  18. Isermann, R., & Balle, P. (1997). Trends in the Application of Model-Based Fault Detection and Diagnosis of Technical Processes. Control Engineering Practice, 5(5), 709-719.

Publication Details

Published in : Volume 2 | Issue 1 | January-February 2016
Date of Publication : 2016-01-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 263-272
Manuscript Number : IJSRST1611216
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

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), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 2, Issue 1, pp.263-272, January-February-2016.
Journal URL : https://ijsrst.com/IJSRST1611216
Citation Detection and Elimination     |      | | BibTeX | RIS | CSV

Article Preview