Predictive Maintenance in Industrial IoT Using Machine Learning Approach
Keywords:
Predictive Maintenance, vibration, proactive, nonintrusive, Jupyter, Machine LearningAbstract
Predictive maintenance utilising Machine learning approaches assist machines or systems in predicting and reducing various forms of machine failures using various particular strategies. Predictive maintenance (PdM) has developed as a crucial strategy to optimising maintenance procedures and enhancing industrial equipment dependability and efficiency. Predictive maintenance, which employs machine learning techniques, helps firms to proactively identify and handle possible equipment faults, decreasing unplanned downtime, lowering maintenance costs, and increasing operational productivity.
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Kaorlis Liulys, “Machine Learning Application in Predictive Maintenance,” 2019 Open Conference of Electrical, Electronic and Information Sciences (es- Tream), pp. 1-4, 2019
Joel Anto Williams N et al., “Machine Predcitive Mainteneace System for Industrial Applications,” International Journal of Current Research, vol. 14, no. 05, pp. 21410-21412, 2022
Archit P. Kane et al., “Predictive Maintenance Using Machine Learning,” ArXiv, 2022.
Marwin Zufle et al., “A Predictive Maintenance Methodology: Predicting the Time-to- Failure of Machines in Industry 4.0,” IEEE 19th International Conference on Industrial Informatics (INDIN), pp. 1-8, 2021
Carvalho, T.P.; Soares, F.A.; Vita, R.; Francisco, R.D.; Basto, J.P.; Alcalá, S.G. A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 2019, 137, 106024
H. M. Hashemian and W. C. Bean, “State-of-theart predictive maintenance techniques,” IEEE Transactions on Instrumentation and measurement, vol. 60, no. 10, pp. 3480–3492, 2019
B. Lu, D. B. Durocher, and P. Stemper, “Predictive maintenance techniques,” IEEE Industry Applications Magazine, vol. 15, no. 6, 2019.
G. A. Susto, A. Schirru, S. Pampuri, S. McLoone, and A. Beghi, “Machine learning for predictive maintenance: A multiple classifier approach,” IEEE Transactions on Industrial Informatics, vol. 11, no. 3, pp. 812–820, 2020
L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2019
F. E. Tay and L. Cao, “Application of support vector machines in financial time series forecasting,” Omega, vol. 29, no. 4, pp. 309–317, 2019.
Susto, Gian Antonio, Andrea Schirru, Simone Pampuri, Sean McLoone, and Alessandro Beghi. "Machine Learning for Predictive Maintenance: A Multiple Classifier Approach", IEEE Transactions on Industrial Informatics, 2015
Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., & Loncarski, J. (2018). Machine Learning approach for Predictive Maintenance in Industry 4.0. 2018
Abishekraj, N., Jeeva Prashanna, G. R., Suriyaa, M. S., Barathraj, T., and Mohanraj, D. , Condition based monitoring for fault detection in windmill gear box using artificial neural network. Paper presented at the IOP Conference Series: Materials Science and Engineering, , 912(3), 2020.
Aji, A. S., Sashiomarda, J. A., and Handoko, D. , Predictive maintenance magnetic sensor using random forest method. Paper presented at the Journal of Physics: Conference Series, 1528(1) , 2020.
B. Bajic, A. Rikalovic, N. Suzic and V. Piuri, "Industry 4.0 Implementation Challenges and Opportunities: A Managerial Perspective," in IEEE Systems Journal, vol. 15, no. 1, pp. 546-559, 2021
B. Nikolic, J. Ignjatic, N. Suzic, B. Stevanov, and A. Rikalovic, “Predictive manufacturing systems in industry 4.0: Trends, benefits and challenges,” in Proc. 28th DAAAM Int. Symp. Intell. Manuf. Autom., pp. 769–802, 2017
M. Piccarozzi, B. Aquilani, and C. Gatti, “Industry 4.0 in management studies: A systematic literature review,” Sustainability, vol. 10, no. 10, pp. 1–24, 2018
M. Ghobakhloo, “The future of manufacturing industry: A strategic roadmap,” J. Manuf. Technol. Manage., vol. 29, no. 6, pp. 910–936, 2018.
M. Piccarozzi, B. Aquilani, and C. Gatti, “Industry 4.0 in management studies: A systematic literature review,” Sustainability, vol. 10, no. 10, pp. 1–24, 2018.
F. Longo, L. Nicoletti, and A. Padovano, “Smart operators in industry4.0: A human-centered approach to enhance operators’ capabilities andcompetencies within the new smart factory context,” Comput. Ind. Eng., vol. 113, pp. 144–159, 2017.
D. Mourtzis and E. Vlachou, “A cloud-based cyber-physical system for adaptive shop-floor scheduling and condition-based maintenance,” J. Manuf. Syst., vol. 47, pp. 179–198, 2018.
Sutton, R.S.; Barto, A.G. Reinforcement Learning: An Introduction; MIT Press: Cambridge, MA, USA, 2018
Wollschlaeger, M.; Sauter, T.; Jasperneite, J. The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0. IEEE Ind. Electron. Mag. 2017
Oztemel, E.; Gursev, S. Literature review of Industry 4.0 and related technologies. J. Intell. Manuf. 2020
Bertolini, M.; Mezzogori, D.; Neroni, M.; Zammori, F. Machine Learning for industrial applications: A comprehensive literature review. Expert Syst. Appl. 2021
Abishekraj, N., Jeeva Prashanna, G. R., Suriyaa, M. S., Barathraj, T., and Mohanraj, D. , Condition based monitoring for fault detection in windmill gear box using artificial neural network. Paper presented at the IOP Conference Series: Materials Science and Engineering, 912(3), 2020.
Aji, A. S., Sashiomarda, J. A., and Handoko, D. Predictive maintenance magnetic sensor using random forest method. Paper presented at the Journal of Physics: Conference Series, 1528(1) , 2020.
Andrei, A. G., Balasa, R., Costea, M. L., and Semenescu, A, Building a blockchain for aviation maintenance records. Paper presented at the Journal of Physics: Conference Series, 1781(1) , 2021
Mohanty, A., and Ranjana, P, Usage of predictive research on further business. International Journal of Innovative Technology and Exploring Engineering, 8(11), 3464-3466, 2019.
Patel, H. K., Shah, D., and Raghuwanshi, A., Real time machine health monitoring and vibrational analysis using FFT approach. International Journal of Engineering and Advanced Technology, 8(5), 1833-1836, 2019
Thoben K-D, Wiesner S, Wuest T. “Industrie 4.0” and Smart Manufacturing –A Review of Research Issues and Application Examples. Internantional J Autom Technol 2017;11:4–19. doi:10.20965/ ijat.2017.p0004.
Lu SCY. Machine learning approaches to knowledge synthesis and integration tasks for advanced engineering automation. Comput Ind 1990;15:105–20. doi:10.1016/0166-3615(90)900 88-7.
Wuest T, Weimer D, Irgens C, Thoben K-D. Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 2016;4:23–45. doi:10.1080/21693277.2016.11925 17.
Moyne J, Iskandar J. Big Data Analytics for Smart Manufacturing:Case Studies in Semiconductor Manufacturing. Processes 2017;5:1–20. doi:10.3390/pr5030039.
Lee J, Bagheri B, Kao HA. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett 2015;3:18–23. doi:10.1016/j.mfglet.2014.12.001.
Kaiser KA, Gebraeel NZ. Predictive maintenance management using sensor-based degradation models. IEEE Trans Syst Man, Cybern Part ASystems Humans 2009;39:840–9. doi:10.1109/TSMCA.2009.2016429.
Coleman C, Damofaran S, Deuel E. Predictive maintenance and the smart factory. 2017. doi:https://www2.deloitte.com/content/dam/Deloitte/us/Documents/proc ess-and-operations/us-cons-predictive-maintenance.pdf.
Kang HS, Lee JY, Choi S, Kim H, Park JH, Son JY, et al. Smart manufacturing: Past research, present findings, and future directions. Int J Precis Eng Manuf -Green Technol 2016;3:111–28. doi:10.100 7/s40684-016-0015-5.
Bibhuti Bhushan Behera, Rajani Kanta Mohanty, Binod Kumar Pattanayak, “Attack Detection and Mitigation in Industrial IoT: An Optimized Ensemble Approach” Specialusis Ugdymas / Special Education, Vol-1(43), pp.879-905, 2022.
Agapiou JS, Stephenson DA. Analytical and Experimental Studies of Drill Temperatures. J Eng Ind 1994;116:54–60. doi:10.1115/1.2901809.
DD Ramlowat, BK Pattanayak, Exploring the internet of things (IoT) in education: a review, “Information Systems Design and Intelligent Applications Proceedings of Fifth International Conference ” , India, pp.187-245, 2018.
Bibhuti Bhusana Behera, Rajani Kanta Mohanty, Binod Kumar Pattanayak, “A Deep Fusion Model For Automated Industrial Iot Cyber Attack Detection And Mitigation”, International Journal of Electrical and Electronics Research (IJEER), Vol-10(3), pp.604-613, 2022.
Bibhuti Bhusana Behera, Binod Kumar Pattanayak, Rajani Kanta Mohanty, “Deep Ensemble Model for Detecting Attacks in Industrial IoT”, International Journal of Information Security and Privacy (IJISP), Vol-16(1), 2022.
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