In modern industrial environments, early and accurate machine fault diagnosis is crucial for minimizing downtime, reducing maintenance costs, and ensuring operational safety. This research presents a robust fault classification framework that combines Recursive Feature Elimination with Cross-Validation (RFECV) and Random Forest classifiers to address the challenges of high dimensionality, overfitting, and limited model generalization. The proposed approach begins with comprehensive data preprocessing, followed by RFECV to identify and retain the most relevant features, thereby enhancing model efficiency and accuracy. Subsequently, a Random Forest classifier is trained on this optimized feature set to classify four fault types: No Failure, Power Failure, Tool Wear Failure, and Overstrain Failure. By integrating feature selection with ensemble learning, the framework effectively mitigates high variance and improves robustness under varying operational conditions and data distributions. Experimental results demonstrate that the proposed methodology achieves a high predictive accuracy of 99.2% along with improved computational efficiency, making it highly suitable for real-time fault diagnosis applications in smart manufacturing systems.
N. E. Sepulveda and J. Sinha, “Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines,” Machines, vol. 8, no. 4, p. 66, Oct. 2020, doi: 10.3390/machines8040066.
Z. Li, Y. Wang, and K.-S. Wang, “Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario,” Advances in Manufacturing, vol. 5, no. 4, pp. 377–387, Dec. 2017, doi: 10.1007/s40436-017-0203-8.
Z. Xiao, Z. Cheng, and Y. Li, “A Review of Fault Diagnosis Methods Based on Machine Learning Patterns,” 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing), pp. 1–4, Oct. 2021, doi: 10.1109/phm-nanjing52125.2021.9612779.
J. Cen, Z. Yang, X. Liu, J. Xiong, and H. Chen, “A Review of Data-Driven Machinery Fault Diagnosis Using Machine Learning Algorithms,” Journal of Vibration Engineering & Technologies, vol. 10, no. 7, pp. 2481–2507, Apr. 2022, doi: 10.1007/s42417-022-00498-9.
M. Fernandes, J. M. Corchado, and G. Marreiros, “Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review,” Applied Intelligence, vol. 52, no. 12, pp. 14246–14280, Mar. 2022, doi: 10.1007/s10489-022-03344-3.
J. Luo, Y. Liu, S. Zhang, and J. Liang, “Extreme random forest method for machine fault classification,” Measurement Science and Technology, vol. 32, no. 11, p. 114006, Jul. 2021, doi: 10.1088/1361-6501/ac14f5.
R. K. Patel and V. K. Giri, “Feature selection and classification of mechanical fault of an induction motor using random forest classifier,” Perspectives in Science, vol. 8, pp. 334–337, Sep. 2016, doi: 10.1016/j.pisc.2016.04.068.
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, Jun. 2015, doi: 10.1109/tii.2014.2349359.
G. Xu et al., “Data-Driven Fault Diagnostics and Prognostics for Predictive Maintenance: A Brief Overview,” 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), Aug. 2019, doi: 10.1109/coase.2019.8843068.
J. J. Saucedo-Dorantes, M. Delgado-Prieto, R. A. Osornio-Rios, and R. de Jesus Romero-Troncoso, “Multifault Diagnosis Method Applied to an Electric Machine Based on High-Dimensional Feature Reduction,” IEEE Transactions on Industry Applications, vol. 53, no. 3, pp. 3086–3097, May 2017, doi: 10.1109/tia.2016.2637307.
B.-S. Yang, X. Di, and T. Han, “Random forests classifier for machine fault diagnosis,” Journal of Mechanical Science and Technology, vol.22, no. 9, pp. 1716–1725, Sep. 2008, doi: 10.1007/s12206-008-0603-6.
Misra, P., & Yadav, A. S, Improving the classification accuracy using recursive feature elimination with cross-validation. Int. J. Emerg. Technol, 11(3), 659-665.2020.
Y. Zhao, Z. Liu, Z. Yang, Q. Han, and H. Ma, “Machinery fault diagnosis-oriented regularization for nonlinear system identification: Framework and applications,” Applied Acoustics, vol. 231, p. 110537, Mar. 2025, doi: 10.1016/j.apacoust.2025.110537.
G. Bode, S. Thul, M. Baranski, and D. Müller, “Real-world application of machine-learning-based fault detection trained with experimental data,” Energy, vol. 198, p. 117323, May 2020, doi: 10.1016/j.energy.2020.117323.
L. C. Brito, G. A. Susto, J. N. Brito, and M. A. V. Duarte, “An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery,” Mechanical Systems and Signal Processing, vol. 163, p. 108105, Jan. 2022, doi:10.1016/j. ymssp.2021.108105.
X. Chen, M. Wang, and H. Zhang, “Machine Learning-based Fault Prediction and Diagnosis of Brushless Motors,” Engineering Advances, vol. 4, no. 3, pp. 130–142, Aug. 2024, doi: 10.26855/ea.2024.07.004.
S. Tang, S. Yuan, and Y. Zhu, “Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery,” IEEE Access, vol.8, pp. 9335–9346, 2020, doi: 10.1109/access.2019.2963092.
D. Gonzalez-Jimenez, J. del-Olmo, J. Poza, F. Garramiola, and I. Sarasola, “Machine Learning-Based Fault Detection and Diagnosis of Faulty Power Connections of Induction Machines,” Energies, vol. 14, no. 16, p. 4886, Aug. 2021, doi: 10.3390/en14164886.
M.-Q. Tran, M. Elsisi, K. Mahmoud, M.-K. Liu, M. Lehtonen, and M. M. F. Darwish, “Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment,” IEEE Access, vol. 9, pp. 115429– 115441, 2021, doi: 10.1109/access.2021.3105297.
R. R. Shubita, A. S. Alsadeh, and I. M. Khater, “Fault Detection in Rotating Machinery Based on Sound Signal Using Edge Machine Learning,” IEEE Access, vol. 11, pp. 6665–6672, 2023, doi: 10.1109/access.2023.3237074.
S. Chen, Y. Meng, H. Tang, Y. Tian, N. He, and C. Shao, “Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery,” IEEE/ASME Transactions on Mechatronics, vol. 25, no. 5, pp. 2167–2176, Oct. 2020, doi: 10.1109/tmech.2020.3007441.
S.-Y. Shao, W.-J. Sun, R.-Q. Yan, P. Wang, and R. X. Gao, “A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing,” Chinese Journal of Mechanical Engineering, vol. 30, no. 6, pp. 1347–1356, Oct. 2017, doi: 10.1007/s10033-017-0189-y.
M. Sohaib, C.-H. Kim, and J.-M. Kim, “A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis,” Sensors, vol. 17, no.12, p. 2876, Dec. 2017, doi: 10.3390/s17122876.
Kafeel et al., “An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis,” Sensors, vol. 21, no. 22, p. 7587, Nov. 2021, doi: 10.3390/s21227587.
C.-W. Hung, C.-H. Lee, C.-C. Kuo, and S.-X. Zeng, “SoC-Based Early Failure Detection System Using Deep Learning for Tool Wear,” IEEE Access, vol. 10, pp. 70491–70501, 2022, doi: 10.1109/access.2022.3187043.
P. F. Orrù, A. Zoccheddu, L. Sassu, C. Mattia, R. Cozza, and S. Arena, “Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry,” Sustainability, vol. 12, no. 11, p. 4776, Jun. 2020, doi:10.3390/su12114776.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Vetrithangam D, Shamik Palit, Anshu Mehta, Gaddam Saranya, Donamol Joseph and Abhinav Pathak;
Writing- Original Draft Preparation: Vetrithangam D, Shamik Palit, Anshu Mehta, Gaddam Saranya, Donamol Joseph and Abhinav Pathak;
Visualization: Gaddam Saranya, Donamol Joseph and Abhinav Pathak;
Investigation: Vetrithangam D, Shamik Palit and Anshu Mehta;
Supervision: Gaddam Saranya, Donamol Joseph and Abhinav Pathak;
Validation: Donamol Joseph and Abhinav Pathak;
Writing- Reviewing and Editing: Vetrithangam D, Shamik Palit, Anshu Mehta, Gaddam Saranya, Donamol Joseph and Abhinav Pathak; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Vetrithangam D
Department of Computer Science & Engineering, Chandigarh University, Punjab, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Vetrithangam D, Shamik Palit, Anshu Mehta, Gaddam Saranya, Donamol Joseph and Abhinav Pathak, “Machine Fault Diagnosis Using Random Forest with Recursive Feature Elimination and Cross Validation”, Journal of Machine and Computing, vol.5, no.3, pp. 1700-1711, July 2025, doi: 10.53759/7669/jmc202505134.
