Rotor system fault detection utilizing semi-supervised and unsupervised machine learning
Corresponding Author(s) : Nima Rezazadeh
Future Energy,
Vol. 4 No. 3 (2025): August 2025 Issue
Abstract
Detecting multiple simultaneous faults in rotor systems is challenging, especially when labelled data is limited. This paper presents a novel framework combining unsupervised and semi-supervised machine learning to enhance fault diagnosis in rotor systems with various fault types. Using finite element method simulations, 100 vibration signal observations were generated for rotor systems under three fault conditions: imbalance, imbalance with shaft bending, and imbalance with cracking. Features were extracted via a multi-layer autoencoder in an unsupervised manner, followed by sequential feature selection to identify the most informative attributes. Two classification approaches were then applied: k-means clustering for unsupervised fault detection and a semi-supervised model with a Softmax layer for classification. The semi-supervised method achieved over 95% accuracy using only three selected features, effectively distinguishing different fault types. In contrast, the unsupervised approach proved better suited for anomaly detection rather than precise fault identification. These results demonstrate the potential of integrating unsupervised feature extraction with semi-supervised classification for reliable fault diagnosis in rotor systems with scarce labelled data.
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- A. Khadersab and S. Shivakumar, “Vibration Analysis Techniques for Rotating Machinery and its effect on Bearing Faults,” 2nd International Conference on Materials, Manufacturing and Design Engineering (iCMMD2017), 11-12 December 2017, MIT Aurangabad, Maharashtra, INDIA, vol. 20, pp. 247–252, Jan. 2018, doi: 10.1016/j.promfg.2018.02.036.
- A. R. Mohanty, Machinery Condition Monitoring: Principles and Practices, 1st ed. Boca Raton: CRC Press, 2014. [Online]. Available: https://doi.org/10.1201/9781351228626
- A. Lorenz, B. Siewertsen, V. Kyhe Clemmensen, J. Blaamann Petersen, J. Friederich, and S. Lazarova-Molnar, “Vibration Data Analysis for Fault Detection in Manufacturing Systems - A Systematic Literature Review,” in 2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA), Dec. 2022, pp. 851–857. doi: 10.1109/ICIEA54703.2022.10006127.
- N. Rezazadeh, A. De Luca, G. Lamanna, and F. Caputo, “Diagnosing and Balancing Approaches of Bowed Rotating Systems: A Review,” 2022. doi: 10.3390/app12189157.
- N. Rezazadeh, D. Perfetto, M. de Oliveira, A. De Luca, and G. Lamanna, “A fine-tuning deep learning framework to palliate data distribution shift effects in rotary machine fault detection,” Struct Health Monit, Nov. 2024, doi: 10.1177/14759217241295951.
- L. S. Jablon, S. L. Avila, B. Borba, G. L. Mourão, F. L. Freitas, and C. A. Penz, “Diagnosis of rotating machine unbalance using machine learning algorithms on vibration orbital features,” Journal of Vibration and Control, vol. 27, no. 3–4, pp. 468–476, Feb. 2021, doi: 10.1177/1077546320929830.
- M. Wisal and K.-Y. Oh, “A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft,” Sensors, vol. 23, no. 16, 2023, doi: 10.3390/s23167141.
- C. E. Rodrigues, C. L. N. Júnior, and D. A. Rade, “Application of Machine Learning Techniques and Spectrum Images of Vibration Orbits for Fault Classification of Rotating Machines,” Journal of Control, Automation and Electrical Systems, vol. 33, no. 1, pp. 333–344, Feb. 2022, doi: 10.1007/s40313-021-00805-x.
- N. Rezazadeh, A. Felaco, S. Fallahy, and G. Lamanna, “Application of Supervised and Unsupervised Machine Learning to the Classification of Damaged Rotor-Bearing Systems,” Macromol Symp, vol. 411, no. 1, 2023, doi: 10.1002/masy.202200219.
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- K. Yu, T. R. Lin, H. Ma, X. Li, and X. Li, “A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning,” Mech Syst Signal Process, vol. 146, p. 107043, Jan. 2021, doi: 10.1016/j.ymssp.2020.107043.
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- X. Wu, Y. Zhang, C. Cheng, and Z. Peng, “A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery,” Mech Syst Signal Process, vol. 149, p. 107327, Feb. 2021, doi: 10.1016/j.ymssp.2020.107327.
- H. D. Nelson and J. M. McVaugh, “The Dynamics of Rotor-Bearing Systems Using Finite Elements,” Journal of Engineering for Industry, vol. 98, no. 2, pp. 593–600, May 1976, doi: 10.1115/1.3438942.
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- R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Insights Imaging, vol. 9, no. 4, pp. 611–629, Aug. 2018, doi: 10.1007/s13244-018-0639-9.
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- S. Lei, X. Lei, M. Chen, and Y. Pan, “Drug Repositioning Based on Deep Sparse Autoencoder and Drug–Disease Similarity,” Interdiscip Sci, vol. 16, no. 1, 2024, doi: 10.1007/s12539-023-00593-9.
- H. Bai, X. Zhan, H. Yan, L. Wen, Y. Yan, and X. Jia, “Research on Diesel Engine Fault Diagnosis Method Based on Stacked Sparse Autoencoder and Support Vector Machine,” Electronics (Switzerland), vol. 11, no. 14, 2022, doi: 10.3390/electronics11142249.
- Prastika, E., Sumarno, S., Masruro, Z., Gunawan, I., & Purnamasari, I. (2022). PENERAPAN DATA MINING DALAM MENGANALISA TINGKAT PEROKOK BERDASARKAN USIA MENGGUNAKAN ALGORITMA K-MEANS CLUSTRING. SmartAI: Buletin artificial intelligence, 1(2), 48-54.
- J. A. Hartigan and M. A. Wong, “Algorithm AS 136: A K-Means Clustering Algorithm,” J R Stat Soc Ser C Appl Stat, vol. 28, no. 1, pp. 100–108, 1979, doi: 10.2307/2346830.
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References
A. Khadersab and S. Shivakumar, “Vibration Analysis Techniques for Rotating Machinery and its effect on Bearing Faults,” 2nd International Conference on Materials, Manufacturing and Design Engineering (iCMMD2017), 11-12 December 2017, MIT Aurangabad, Maharashtra, INDIA, vol. 20, pp. 247–252, Jan. 2018, doi: 10.1016/j.promfg.2018.02.036.
A. R. Mohanty, Machinery Condition Monitoring: Principles and Practices, 1st ed. Boca Raton: CRC Press, 2014. [Online]. Available: https://doi.org/10.1201/9781351228626
A. Lorenz, B. Siewertsen, V. Kyhe Clemmensen, J. Blaamann Petersen, J. Friederich, and S. Lazarova-Molnar, “Vibration Data Analysis for Fault Detection in Manufacturing Systems - A Systematic Literature Review,” in 2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA), Dec. 2022, pp. 851–857. doi: 10.1109/ICIEA54703.2022.10006127.
N. Rezazadeh, A. De Luca, G. Lamanna, and F. Caputo, “Diagnosing and Balancing Approaches of Bowed Rotating Systems: A Review,” 2022. doi: 10.3390/app12189157.
N. Rezazadeh, D. Perfetto, M. de Oliveira, A. De Luca, and G. Lamanna, “A fine-tuning deep learning framework to palliate data distribution shift effects in rotary machine fault detection,” Struct Health Monit, Nov. 2024, doi: 10.1177/14759217241295951.
L. S. Jablon, S. L. Avila, B. Borba, G. L. Mourão, F. L. Freitas, and C. A. Penz, “Diagnosis of rotating machine unbalance using machine learning algorithms on vibration orbital features,” Journal of Vibration and Control, vol. 27, no. 3–4, pp. 468–476, Feb. 2021, doi: 10.1177/1077546320929830.
M. Wisal and K.-Y. Oh, “A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft,” Sensors, vol. 23, no. 16, 2023, doi: 10.3390/s23167141.
C. E. Rodrigues, C. L. N. Júnior, and D. A. Rade, “Application of Machine Learning Techniques and Spectrum Images of Vibration Orbits for Fault Classification of Rotating Machines,” Journal of Control, Automation and Electrical Systems, vol. 33, no. 1, pp. 333–344, Feb. 2022, doi: 10.1007/s40313-021-00805-x.
N. Rezazadeh, A. Felaco, S. Fallahy, and G. Lamanna, “Application of Supervised and Unsupervised Machine Learning to the Classification of Damaged Rotor-Bearing Systems,” Macromol Symp, vol. 411, no. 1, 2023, doi: 10.1002/masy.202200219.
S. Ma and F. Chu, “Ensemble deep learning-based fault diagnosis of rotor bearing systems,” Comput Ind, vol. 105, pp. 143–152, Feb. 2019, doi: 10.1016/j.compind.2018.12.012.
K. Yu, T. R. Lin, H. Ma, X. Li, and X. Li, “A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning,” Mech Syst Signal Process, vol. 146, p. 107043, Jan. 2021, doi: 10.1016/j.ymssp.2020.107043.
J. Yuan, R. Zhao, T. He, P. Chen, K. Wei, and Z. Xing, “Fault diagnosis of rotor based on Semi-supervised Multi-Graph Joint Embedding,” ISA Trans, vol. 131, pp. 516–532, Dec. 2022, doi: 10.1016/j.isatra.2022.05.006.
X. Wu, Y. Zhang, C. Cheng, and Z. Peng, “A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery,” Mech Syst Signal Process, vol. 149, p. 107327, Feb. 2021, doi: 10.1016/j.ymssp.2020.107327.
H. D. Nelson and J. M. McVaugh, “The Dynamics of Rotor-Bearing Systems Using Finite Elements,” Journal of Engineering for Industry, vol. 98, no. 2, pp. 593–600, May 1976, doi: 10.1115/1.3438942.
F. D. Sanches and R. Pederiva, “Theoretical and experimental identification of the simultaneous occurrence of unbalance and shaft bow in a Laval rotor,” Mech Mach Theory, vol. 101, pp. 209–221, 2016, doi: 10.1016/j.mechmachtheory.2016.03.019.
C. A. Papadopoulos and A. D. Dimarogonas, “Stability of Cracked Rotors in the Coupled Vibration Mode,” Journal of Vibration, Acoustics, Stress, and Reliability in Design, vol. 110, no. 3, pp. 356–359, Jul. 1988, doi: 10.1115/1.3269525.
R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Insights Imaging, vol. 9, no. 4, pp. 611–629, Aug. 2018, doi: 10.1007/s13244-018-0639-9.
E. R. Ardelean, A. Coporîie, A. M. Ichim, M. Dînşoreanu, and R. C. Mureşan, “A study of autoencoders as a feature extraction technique for spike sorting,” PLoS One, vol. 18, no. 3 March, 2023, doi: 10.1371/journal.pone.0282810.
S. Lei, X. Lei, M. Chen, and Y. Pan, “Drug Repositioning Based on Deep Sparse Autoencoder and Drug–Disease Similarity,” Interdiscip Sci, vol. 16, no. 1, 2024, doi: 10.1007/s12539-023-00593-9.
H. Bai, X. Zhan, H. Yan, L. Wen, Y. Yan, and X. Jia, “Research on Diesel Engine Fault Diagnosis Method Based on Stacked Sparse Autoencoder and Support Vector Machine,” Electronics (Switzerland), vol. 11, no. 14, 2022, doi: 10.3390/electronics11142249.
Prastika, E., Sumarno, S., Masruro, Z., Gunawan, I., & Purnamasari, I. (2022). PENERAPAN DATA MINING DALAM MENGANALISA TINGKAT PEROKOK BERDASARKAN USIA MENGGUNAKAN ALGORITMA K-MEANS CLUSTRING. SmartAI: Buletin artificial intelligence, 1(2), 48-54.
J. A. Hartigan and M. A. Wong, “Algorithm AS 136: A K-Means Clustering Algorithm,” J R Stat Soc Ser C Appl Stat, vol. 28, no. 1, pp. 100–108, 1979, doi: 10.2307/2346830.
J. Barrera-García, F. Cisternas-Caneo, B. Crawford, M. Gómez Sánchez, and R. Soto, “Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications,” 2024. doi: 10.3390/biomimetics9010009.