Prediction of Machine Failure Status Using Machine Learning Techniques
DOI:
https://doi.org/10.32628/IJSRST251262Keywords:
Machine Failure, Machine learingAbstract
This abstract presents a study on predicting machine failure status using machine learning techniques. With the increasing complexity of industrial systems, early detection of machinery failures is crucial for maintaining operational efficiency and minimizing downtime. In this research, various machine learning algorithms are employed to analyse historical sensor data and identify patterns indicative of impending failures. The proposed approach demonstrates significant potential in accurately predicting machine failures, thus enabling proactive maintenance strategies. Experimental results showcase the effectiveness of the model in achieving high accuracy and precision in predicting failure conditions across diverse industrial settings. This work contributes to the field of predictive maintenance by harnessing the power of machine learning to enhance operational reliability and optimize maintenance schedules.Then Industrial equipment performance control and failure prediction are important not just for the quality of the produced material, but also for the amount of time and money saved in overall maintenance. This project aims to monitor the evolution of AI/ML techniques for equipment fault prediction in industries over time. The topics covered in this paper include machine learning algorithms, use cases, and principles related to the application of such technology in a variety of industries such as software and hardware.
📊 Article Downloads
References
O. Beaumont, L. Eyraud-Dubois, and J. A. Lorenzo-Del Castillo, Analyzing real cluster data for formulating allo cation algorithms in cloud platforms, Parallel Comput., vol. 54, pp. 8396, 2020.
K. Singh, S. Smallen, S. Tilak, and L. Saul, Failure anal ysis and prediction for the CIPRES science gateway Kri tika, Concurr. Comput. Pract. Exp., vol. 22, no. 6, pp. 685701, 2019.
P. Garraghan, P. Townend, and J. Xu, An empirical failure-analysis of a large-scale cloud computing environ ment, Proc.- 2014 IEEE 15th Int. Symp. High-Assurance Syst. Eng. HASE 2019, pp. 113120, 2021.
J. Elliott, K. Kharbas, D. Fiala, F. Mueller, K. Ferreira, and C. Engelmann, Combining partial redundancy and checkpointing for HPC, Proc.- Int. Conf. Distrib. Com put. Syst., pp. 615626, 2020.
B. Mohammed, M. Kiran, K. M. Maiyama, M. M. Ka mala, and I.-U. Awan, Failover strategy for fault tol erance in cloud computing environment, Softw. Pract. Exp., 2020.
R. Ghosh, L. Francesco, F. Frattini, S. Russo, and S. T. Kishor, Scalable analytics for IaaS cloud availability, IEEETrans. Cloud Comput., vol. 2, no. 1, pp. 5770, 2021. DOI: https://doi.org/10.1109/TCC.2014.2310737
T. Chalermarrewong, T. Achalakul, and S. C. W. See, TheDesign of a Fault Management Framework for Cloud, 2021 9th Int. Conf. Electr. Eng. Comput. Telecommun. Inf. Technol., pp. 14, 2021.
A.Elzamly, B. Hussin, A. Samad, H. Basari, and C. Tech nology, Classification of Critical Cloud Computing Secu rity Issues for Banking Organizations: A cloud Delphi Study, Int. J. Grid Distrib. Comput., vol. 9, no. 8, pp. 137158, 2021. DOI: https://doi.org/10.14257/ijgdc.2016.9.8.13
ITProPortal, ITProPortal.com: 24/7 Tech Commentary and Analysis, 2020. [Online]. Available: http://www. it proportal.com/. [Accessed: 24-Jun-2020].
Bilal K, Khalid O, Malik SU, Khan MUS, Khan S, Zomaya A. Fault tolerance in the cloud. In Fault Tol erance in the Cloud Encyclopedia on Cloud Comput ing, vol. 2020. John Wiley and Sons: Hoboken, NJ, USA, 2020: 291300. 11. C. Modi, D. Patel, B. Borisaniya, A. Patel, and M. Ra jarajan, A survey on security issues and solutions at dif ferent layers of Cloud computing, J. Supercomput., vol. 63, no. 2, pp. 561592, 2020.
D. Gnanavelu and D. G. Gunasekaran, Survey on Se curity Issues and Solutions in Cloud Computing, Int. J. Comput. Trends Technol., vol. 8, no. 8, pp. 126130, 2020.
B. Wang, Y. Zheng, W. Lou, and Y. T. Hou, DDoS attack protection in the era of cloud computing and Software-Defined Networking, Comput. Networks, vol. 81, pp. 308319, 2020. DOI: https://doi.org/10.1016/j.comnet.2015.02.026
Z. Pantic and M. Babar, Guidelines for Building a Private Cloud Infrastructure, ITU Tech. Rep.- TR-2012-153TR 2012-153, 2021.
O. Sefraoui, M. Aissaoui, and M. Eleuldj, Cloud com puting migration and IT resources rationalization, 2014 Int. Conf. Multimed. Comput. Syst., pp. 11641168, Apr. 2020. DOI: https://doi.org/10.1109/ICMCS.2014.6911300
A. Sen and S. Madria, Off-Line Risk Assessment of Cloud Service Provider, 2014 IEEE World Congr. Serv., pp. 5865, Jun. 2020. DOI: https://doi.org/10.1109/SERVICES.2014.20
S. Yadav, Comparative Study on Open Source Software for Cloud Computing Platform: Eucalyptus , Openstack and Opennebula, Res. Inven. Int. J. Eng. Sci. Vol.3, Issue 10, vol. 3, no. 10, pp. 5154, 2020.
G. Bontempi, S. Ben Taieb, and Y. A. Le Borgne, Ma chine learning strategies for time series forecasting, Lect. Notes Bus. Inf. Process., vol. 138 LNBIP, pp. 6277, 2020.
A. Chigurupati, R. Thibaux, and N. Lassar, Predicting hardware failure using machine learning, 2016 Annu. Re liab. Maintainab. Symp., pp. 16, 2019. 19. E. Fulp, G. Fink, and J. Haack, Predicting Computer System Failures Using Support Vector Machines., Proc. First USENIX Conf. Anal. Syst. logs, pp. 55, 2020.
O. Beaumont, L. Eyraud-Dubois, and J. A. Lorenzo-Del Castillo, Analyzing real cluster data for formulating allo cation algorithms in cloud platforms, Parallel Comput., vol. 54, pp. 8396, 2022. DOI: https://doi.org/10.1016/j.parco.2015.07.001
K. Singh, S. Smallen, S. Tilak, and L. Saul, Failure anal ysis and prediction for the CIPRES science gateway Kri tika, Concurr. Comput. Pract. Exp., vol. 22, no. 6, pp. 685701, 2022.
P. Garraghan, P. Townend, and J. Xu, An empirical failure-analysis of a large-scale cloud computing environ ment, Proc.- 2021 IEEE 15th Int. Symp. High-Assurance Syst. Eng. HASE 2014, pp. 113120, 2021. DOI: https://doi.org/10.1109/HASE.2014.24
J. Elliott, K. Kharbas, D. Fiala, F. Mueller, K. Ferreira, and C. Engelmann, Combining partial redundancy and checkpointing for HPC, Proc.- Int. Conf. Distrib. Com put. Syst., pp. 615626, 2022.
B. Mohammed, M. Kiran, K. M. Maiyama, M. M. Ka mala, and I.-U. Awan, Failover strategy for fault tol erance in cloud computing environment, Softw. Pract. Exp., 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
https://creativecommons.org/licenses/by/4.0
