Feature-Based Fault Detection and Degradation Analysis of Ball Bearings under Run-to-Failure Conditions

Ekta Gupta, Paridhi Gupta, Swetha Rajkumar, Mogana Priya Chinnasamy

Abstract

Reliable fault detection and degradation assessment of rolling element bearings are crucial for condition-based maintenance of rotating machinery. This paper presents a feature-based approach for fault detection and degradation analysis of ball bearings operating under run-to-failure conditions. Vibration signals measured along two orthogonal directions were processed to extract RMS features. It captures directional vibration behavior, and a combined RMS-based degradation index was constructed, providing a representation of bearing health. Statistical thresholds were established from the healthy operating region, and a persistence-based detection criterion was applied to identify fault onset while avoiding false alarms caused by transient fluctuations. The results show that RMS vibration features are sensitive to early mechanical degradation. It exhibits a sustained increase before bearing failure. Directional analysis shows clear vibration responses along different axes, which supports using a combined degradation index for dependable fault detection. Temperature analysis shows a delayed response compared to vibration indicators. A significant temperature rise happens in the later stage of bearing life, confirming the level of degradation. This proposed method offers a clear and effective way to detect bearing faults when running to failure and serves as a practical foundation for future studies on predicting remaining useful life.

Keywords

References

[1] M. Pandiyan and T. Narendiranath, “Systematic review on fault diagnosis on rolling-element bearing,” Journal of Vibration Engineering & Technologies, vol. 12, no. 7, pp. 8249–8283, Oct. 2024, doi: 10.1007/s42417-024-01358-4.

[2] Y. Liu, J. Wen, and G. Wang, “A comprehensive overview of remaining useful life prediction: From traditional literature review to scientometric analysis,” Machine Learning with Applications, vol. 21, art. no. 100704, Sep. 2025, doi: 10.1016/j.mlwa.2025.100704.

[3] A. Qin, Q. Zhang, Q. Hu, G. Sun, J. He, and S. Lin, “Remaining useful life prediction for rotating machinery based on optimal degradation indicator,” Shock and Vibration, vol. 2017, art. no. 6754968, Mar. 2017, doi: 10.1155/2017/6754968.

[4] A. Kumar, C. Parkash, H. Tang, and J. Xiang, “Intelligent framework for degradation monitoring, defect identification and estimation of remaining useful life (RUL) of bearing,” Advanced Engineering Informatics, vol. 58, art. no. 102206, Oct. 2023, doi: 10.1016/j.aei.2023.102206.

[5] R. K. Singleton, E. G. Strangas, and S. Aviyente, “Extended Kalman filtering for remaining-useful-life estimation of bearings,” IEEE Transactions on Industrial Electronics, vol. 62, no. 3, pp. 1781–1790, Mar. 2015, doi: 10.1109/TIE.2014.2336616.

[6] L. Cui, X. Wang, H. Wang, and J. Ma, “Research on remaining useful life prediction of rolling element bearings based on time-varying Kalman filter,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 6, pp. 2858–2867, Jun. 2020, doi: 10.1109/TIM.2019.2924509.

[7] M. Yan, X. Wang, B. Wang, M. Chang, and I. Muhammad, “Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model,” ISA Transactions, vol. 98, pp. 471–482, Mar. 2020, doi: 10.1016/j.isatra.2019.08.058.

[8] H. Ding, L. Yang, Z. Cheng, and Z. Yang, “A remaining useful life prediction method for bearing based on deep neural networks,” Measurement, vol. 172, art. no. 108878, Feb. 2021, doi: 10.1016/j.measurement.2020.108878.

[9] L. Guo, N. Li, F. Jia, Y. Lei, and J. Lin, “A recurrent neural network based health indicator for remaining useful life prediction of bearings,” Neurocomputing, vol. 240, pp. 98–109, May 2017, doi: 10.1016/j.neucom.2017. 02.045

[10] L. Ren, Y. Sun, H. Wang, and L. Zhang, “Prediction of bearing remaining useful life with deep convolution neural network,” IEEE Access, vol. 6, pp. 13041–13049, Feb. 2018, doi: 10.1109/ACCESS.2018.2804930.

[11] C. Cheng, G. Ma and Y. Zhang, et al., “A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings”, IEEE/ASME Transactions on Mechatronics, vol. 25, no. 3, pp. 1243-1254, June 2020, doi: 10.1109/TMECH.2020.2971503.

[12] Y. Chen, G. Peng, Z. Zhu, and S. Li, “A novel deep learning method based on attention mechanism for bearing remaining useful life prediction,” Applied Soft Computing, vol. 86, art. no. 105919, Jan. 2020, doi: 10.1016/j.asoc.2019.105919.

[13] T. Touret, C. Changenet, F. Ville, M. Lalmi, and S. Becquerelle, “On the use of temperature for online condition monitoring of geared systems - A review,” Mechanical Systems and Signal Processing, vol. 101, pp. 197–210, Feb. 2018, doi: 10.1016/j.ymssp.2017.07.044.

[14] N. D. Thuan, T. P. Dong, H. T. Nguyen, and H. S. Hoang, “Efficient bearing fault diagnosis with neural network search and parameter quantization based on vibration and temperature,” Engineering Research Express, vol. 5, no. 2, art. no. 025044, May 2023, doi: 10.1088/2631-8695/acd625.

[15] W. Jung, S. H. Yun, and Y. H. Park, “Vibration and temperature run-to-failure dataset of ball bearing for prognostics,” Data in Brief, vol. 54, art. no. 110403, Jun. 2024, doi: 10.1016/j.dib.2024. 110403.

[16] A. A. Nayeeif, E. S. Al-Ameen, N. A. Jebur, A. A. F. Ogaili, Z. K. Hamdan, and E. K. Njim, “Investigation of the effects of unbalance and bearing wear on shaft vibration in a natural gas turbine plant,” Applied Science and Engineering Progress, vol. 18, no. 4, Jul. 2025, art. no. 7863, doi: 10.14416/j.asep.2025.07.012.

[17] N. Tandon and A. Choudhury, “A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings,” Tribology International, vol. 32, no. 8, pp. 469–480, Aug. 1999, doi: 10.1016/ S0301-679X(99)00077-8.

[18] Y. Lei, N. Li, L. Guo, N. Li, T. Yan, and J. Lin, “Machinery health prognostics: A systematic review from data acquisition to RUL prediction,” Mechanical Systems and Signal Processing, vol. 104, pp. 799–834, May 2018, doi: 10.1016/j.ymssp.2017.11.016.

[19] J. Coble and J. W. Hines, “Identifying optimal prognostic parameters from data using genetic algorithms,” in Proceedings of the Annual Conference of the Prognostics and Health Management Society, San Diego, CA, USA, Mar. 2021, pp. 1–12.

[20] W. Zhang, G. Peng, C. Li, Y. Chen, and Z. Zhang, “A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals,” Sensors, vol. 17, no. 2, art. no. 425, Feb. 2017, doi: 10.3390/s17020425.

[21] A. D. Nembhard, J. K. Sinha, A. J. Pinkerton, and K. Elbhbah, “Condition monitoring of rotating machines using vibration and bearing temperature measurements,” in Advances in Condition Monitoring of Machinery in Non-Stationary Operations, G. Dalpiaz et al., Eds., Lecture Notes in Mechanical Engineering, Berlin, Heidelberg: Springer, 2014, pp. 303–311, doi: 10.1007/978-3-642-39348-8_13.

Full Text: PDF

DOI: 10.14416/j.asep.2026.06.012

Refbacks

There are currently no refbacks.

Username
Password
Remember me