Development of Bearing Fault Detection Models using Multibody Simulation Training Data
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
https://orcid.org/0009-0001-7997-7935
Luigi Ganpio Di Maggio
Eugenio Brusa
Cristiana Delprete
Abstract
This study evaluates the performance of simulation-trained fault detection models on large spherical roller bearings vibration data. A high-fidelity multibody (MB) model of a SKF 22240 CCK/W33 bearing is developed through Simscape Multibody to reproduce the coupled dynamics of inner and outer rings, cage, and 38 rolling elements. Localized defects on raceways are represented through a pointcloud contact formulation, where selected nodes are radially displaced to emulate faults. The model outputs triaxial accelerations at the outer ring under realistic loading and speed conditions that mirror an experimental test campaign.
Simulation signals are processed through bandpass filtering, envelope analysis, and segmentation. A set of 23 time and frequency domain features is extracted from each segment, then each feature vector is normalized. The same processing chain is applied to experimental data acquired on a medium-to-large bearing test rig at Politecnico di Torino, mounting SKF 22240 CCK/W33 bearings with machined inner race, outer race, and rolling element defects.
A supervised Artificial Neural Network (ANN) classifier is trained only on the simulated feature dataset and then directly evaluated on the independent experimental dataset, in a process free of any data transfer. The network addresses a two-class problem (healthy and damaged), and its performance is assessed through standard classification metrics computed over multiple bootstraps of both training and test sets.
Despite the intrinsic differences between simulated and experimental signals, the ANN trained purely on simulations provides reliable and selective fault detection on real measurements. Most residual classification errors are concentrated in low-speed inner race damage conditions, where fault signatures are weak and partially overlap with healthy observations, while high-speed and outer race damage conditions are recognized more robustly.
These results show that MB simulation can generate sufficiently realistic vibration data to train ANN-based fault detection models that generalize experimental measurements for large spherical roller bearings. The proposed framework introduces an alternative to costly fault campaigns and offers a flexible way to expand training datasets across loads, speeds, and defect sizes in industrial condition monitoring applications.
How to Cite
##plugins.themes.bootstrap3.article.details##
Rolling element bearing, Fault detection, Multibody model, Mechanical vibration, Localized faults, Shallow learning, Neural network
Brusa, E., Delprete, C., Giorio, L., Di Maggio, L. G., & Zanella, V. (2022). Design of an innovative test rig for industrial bearing monitoring with self-balancing layout. Machines, 10(54). doi: 10.3390/machines10010054
Di Maggio, L. G., Giorio, L., Delprete, C., & Brusa, E. (2024). Dataset of vibration, temperature and speed measurements for multiple types of localized defects on spherical roller bearings across multiple operating conditions [Data set]. Zenodo. doi: 10.5281/ZENODO.13913254
Giraudo, L., Di Maggio, L. G., Giorio, L., & Delprete, C. (2025). Dynamic multibody modeling of spherical roller bearings with localized defects for large-scale rotating machinery. Sensors, 25(8), 2419. doi: 10.3390/s25082419
Lei, Y., He, Z., Zi, Y., & Hu, Q. (2007). Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mechanical Systems and Signal Processing, 21(5), 2280–2294. doi: 10.1016/j.ymssp.2006.11.003
Lei, Y., Jia, F., Lin, J., Xing, S., & Ding, S. X. (2016). An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Transactions on Industrial Electronics, 63(5), 3137–3147. doi: 10.1109/TIE.2016.2519325
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587. doi: 10.1016/j.ymssp.2019.106587
Liu, C., & Gryllias, K. (2022). Simulation-driven domain adaptation for rolling element bearing fault diagnosis. IEEE Transactions on Industrial Informatics, 18(9), 5760–5770. doi: 10.1109/TII.2021.3103412
Liu, X., Liu, S., Xiang, J., & Sun, R. (2023). A transfer learning strategy based on numerical simulation driving 1D Cycle-GAN for bearing fault diagnosis. Information Sciences, 642, 119175. doi: 10.1016/j.ins.2023.119175
McFadden, P., & Smith, J. (1984). Model for the vibration produced by a single-point defect in a rolling element bearing. Journal of Sound and Vibration, 96(1), 69–82. doi: 10.1016/0022-460X(84)90595-9
Misbah, I., Lee, C., & Keung, K. (2024). Fault diagnosis in rotating machines based on transfer learning: Literature review. Knowledge-Based Systems, 283, 111158. doi: 10.1016/j.knosys.2023.111158
Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. doi: 10.1109/TKDE.2009.191
Smith, W. A., & Randall, R. B. (2015). Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mechanical Systems and Signal Processing, 64–65, 100–131. doi: 10.1016/j.ymssp.2015.04.021
Sobie, C., Freitas, C., & Nicolai, M. (2018). Simulation-driven machine learning: Bearing fault classification. Mechanical Systems and Signal Processing, 99, 403–419. doi: 10.1016/j.ymssp.2017.06.025
Surucu, O., Gadsden, S. A., & Yawney, J. (2023). Condition monitoring using machine learning: A review of theory, applications, and recent advances. Expert Systems with Applications, 221, 119738. doi: 10.1016/j.eswa.2023.119738
Tang, S., Ma, J., Yan, Z., Zhu, Y., & Khoo, B. C. (2024). Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery. Engineering Applications of Artificial Intelligence, 134, 108678. doi: 10.1016/j.engappai.2024.108678
Traganitis, P. A., & Strangas, E. G. (2023). Perspectives of transfer learning on the diagnosis of faults in electrical machines, power electronics, and drives. In 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) (pp. 535–541). IEEE. doi: 10.1109/SDEMPED54949.2023.10271469
Vehviläinen, M., Tahkola, M., Keränen, J., El Bouharrouti, N., Rahkola, P., Halme, J., ... Belahcen, A. (2024). 3D multibody simulation of realistic rolling bearing defects for fault classifier development. In 2024 International Conference on Electrical Machines (ICEM) (pp. 1–7). doi: 10.1109/ICEM60801.2024.10700332
Wang, D., Tse, P. W., & Tsui, K.-L. (2013). An enhanced Kurtogram method for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, 35(1–2), 176–199. doi: 10.1016/J.YMSSP.2012.10.003
Wang, T., Chen, L., Lu, H., Wang, S., Li, Z., Zhang, W., & Mei, J. (2023). Finite element dynamic model with local faults. In Volume 2: Manufacturing Equipment and Automation; Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability (p. V002T06A033). American Society of Mechanical Engineers. doi: 10.1115/MSEC2023-105504
Widodo, A., & Yang, B.-S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 21(6), 2560–2574. doi: 10.1016/J.YMSSP.2006.12.007
Zhao, C., Zhang, Q., Wang, C., & Peng, H. (2023). Digital twin-based bearing fault simulation modeling strategy and display dynamics. In 2023 6th International Symposium on Autonomous Systems (ISAS) (pp. 1–5). IEEE. doi: 10.1109/ISAS59543.2023.10164348
Zhou, J., Zheng, L.-Y., Wang, Y., & Gogu, C. (2020). A multistage deep transfer learning method for machinery fault diagnostics across diverse working conditions and devices. IEEE Access, 8, 80879–80898. doi: 10.1109/ACCESS.2020.2990739
Zhu, Z., Wang, L., Peng, G., & Li, S. (2021). WDA: An improved Wasserstein distance-based transfer learning fault diagnosis method. Sensors, 21(13), 4394. doi: 10.3390/s21134394
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.