Data-Driven Fault Diagnosis of Rolling Bearings Using Neural Networks Optimized via Bayesian, Particle Swarm, and Genetic Algorithm Methods with Time–Frequency Features
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Published
Aug 1, 2026
Khoualdia Kaaïs
Khoualdia Tarek
Mourad Nahal
Abstract
This study presents an applied comparative evaluation of automated rolling bearing fault identification using Artificial Neural Networks (ANNs) optimized through Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Vibration signals were collected from bearings operating under five health conditions (healthy, outer ring fault, inner ring fault, ball fault, and combined faults), at three rotational speeds, and along three measurement directions. The acquired signals were preprocessed using filtering, normalization, and segmentation. Time-domain and Fast Fourier Transform (FFT)-based frequency-domain features were extracted and used to train ANN models. The ANN architectures, including hidden layers, neurons, and activation functions, were optimized using BO, PSO, and GA, resulting in six configurations. Since the problem is a multi-class classification task, performance was assessed using F1-score, Accuracy, precision, and recall. The optimized ANN models were also benchmarked against Support Vector Machine (SVM), K-Nearest Neighbors (kNN), and Random Forest (RF) classifiers using the same feature sets. Results show that FFT -based features consistently outperformed time-domain features, and ANN-PSO with FFT-based features achieved the best performance, with F1-score = 0.982, Accuracy = 0.998, precision = 0.982, and recall = 0.982. This work contributes a systematic applied comparison of ANN optimization strategies rather than a fundamentally new machine-learning architecture, highlighting optimized ANNs as competitive and computationally efficient solutions for bearing fault diagnosis.
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Keywords
Rolling Bearings, Neural Networks, Particle Swarm, Genetic Algorithm
References
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Soares, R. C., Silva, J. C., De Lucena, J. A., Filho, A. C. L., De Souza Ramos, J. G. G., & Brito, A. V. (2025). Integration of Bayesian optimization into hyperparameter tuning of the particle swarm optimization algorithm to enhance neural networks in bearing failure classification. Measurement, 242, 115829. https://doi.org/10.1016/j.measurement.2024.115829
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Wu, Y., Dai, J., Yang, X., Shao, F., Gong, J., Zhang, P., & Liu, S. (2025). The fault diagnosis of rolling bearings based on FFT-SE-TCN-SVM. Actuators, 14(3), 152. https://doi.org/10.3390/act14030152
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An, Y., Zhang, K., Liu, Q., Chai, Y., & Huang, X. (2022b). Rolling bearing fault diagnosis method base on periodic sparse attention and LSTM. IEEE Sensors Journal, 22(12), 12044–12053. https://doi.org/10.1109/jsen.2022.3173446
Chen, Q., Zhang, F., Wang, Y., Yu, Q., Lang, G., & Zeng, L. (2025). Bearing fault diagnosis based on efficient cross space multiscale CNN transformer parallelism. Scientific Reports, 15(1), 12344. https://doi.org/10.1038/s41598-025-95895-x
Chen, Z., Zhu, L., Lu, H., Chen, S., Zhu, F., Liu, S., Han, Y., & Xiong, G. (2024). Research on bearing fault diagnosis based on improved genetic algorithm and BP neural network. Scientific Reports, 14(1), 15527. https://doi.org/10.1038/s41598-024-66318-0
Chi, F., Yang, X., Shao, S., & Zhang, Q. (2022). Bearing fault diagnosis for Time-Varying system using Vibration–Speed fusion network based on Self-Attention and sparse feature extraction. Machines, 10(10), 948. https://doi.org/10.3390/machines10100948
Ding, Y., Jia, M., Miao, Q., & Cao, Y. (2021). A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings. Mechanical Systems and Signal Processing, 168, 108616. https://doi.org/10.1016/j.ymssp.2021.108616
El-Hassani, F. Z., Amri, M., Joudar, N., & Haddouch, K. (2024). A new optimization model for MLP Hyperparameter tuning: Modeling and resolution by Real-Coded Genetic Algorithm. Neural Processing Letters, 56(2). https://doi.org/10.1007/s11063-024-11578-0
Fu, G., Wei, Q., & Yang, Y. (2024b). Bearing fault diagnosis with parallel CNN and LSTM. Mathematical Biosciences & Engineering, 21(2), 2385–2406. https://doi.org/10.3934/mbe.2024105
Gao, S., Shi, S., & Zhang, Y. (2022b). Rolling bearing compound fault diagnosis based on Parameter Optimization MCKD and convolutional neural network. IEEE Transactions on Instrumentation and Measurement, 71, 1–8. https://doi.org/10.1109/TIM.2022.3158379
Guan, J., & Wang, Y. (2025). Fault diagnosis strategy for wind turbine bearings based on improved high-dimensional Bayesian optimized CNN. Journal of Mechanical Science and Technology, 39(11), 6655–6667. https://doi.org/10.1007/s12206-025-1012-9
Hatipoğlu, A., Süpürtülü, M., & Yılmaz, E. (2024). Enhanced Fault Classification in Bearings: A Multi-Domain Feature Extraction Approach with LSTM-Attention and LASSO. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-024-09842-5
Huang, D., Su, X., Yang, J., Du, S., Wang, D., & Ran, Q. (2025). An improved dual-channel CNN-BILSTM fusion attention model for fault diagnosis of aero-engine bearings. Measurement, 117761. https://doi.org/10.1016/j.measurement.2025.117761
Jain, P., & Bhosle, S. (2022). Analysis of vibration signals caused by ball bearing defects using time-domain statistical indicators. International Journal of Advanced Technology and Engineering Exploration, 9(90). https://doi.org/10.19101/ijatee.2021.875416
Jiang, G., Li, D., Feng, K., Li, Y., Zheng, J., Ni, Q., & Li, H. (2023b). Rolling bearing fault diagnosis based on convolutional capsule network. Journal of Dynamics Monitoring and Diagnostics. https://doi.org/10.37965/jdmd.2023.260
Kalay, O. C. (2025). An optimized 1-D CNN-LSTM approach for fault diagnosis of rolling bearings considering epistemic uncertainty. Machines, 13(7), 612. https://doi.org/10.3390/machines13070612
Khoualdia, T., Chelli, Z., & Boukhari, S. (2025b). Multi-response optimization of diagnosis vibration parameters of broken rotor bars in induction motors using grey relational analysis in the Taguchi method. Journal of Vibration Engineering & Technologies, 13(6). https://doi.org/10.1007/s42417-025-01949-9
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2013). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1–2), 314–334. https://doi.org/10.1016/j.ymssp.2013.06.004
Liao, J., He, C., Li, J., Sun, J., Zhang, S., & Zhang, X. (2024a, April 11). Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noise. arXiv.org. https://arxiv.org/abs/2404.15341
Liu, H., Zhang, F., Tan, Y., Huang, L., Li, Y., Huang, G., Luo, S., & Zeng, A. (2024). Multi-scale quaternion CNN and BiGRU with cross self-attention feature fusion for fault diagnosis of bearing. Measurement Science and Technology, 35(8), 086138. https://doi.org/10.1088/1361-6501/ad4c8e
Lou, X., & Loparo, K. A. (2003). Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing, 18(5), 1077–1095. https://doi.org/10.1016/s0888-3270(03)00077-3
Onorato, G. (2024). Bayesian optimization for hyperparameters tuning in neural networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2410.21886
Pan, H., Jiao, W., Yan, T., Rehman, A. U., Wan, A., & Yang, S. (2023b). Combining kernel principal component analysis and spatial group-wise enhance convolutional neural network for fault recognition of rolling element bearings. Measurement Science and Technology, 34(12), 125003. https://doi.org/10.1088/1361-6501/ace790
Qian, B., Cai, Y., Ran, Y., & Sun, W. (2024). Nonlinear mechanical response analysis and convolutional neural network enabled diagnosis of single-span rotor bearing system. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-61180-6
Qiu, B., Liu, S., Li, W., Wang, X. V., & Wang, L. (2025). Dynamic adaptive fault diagnosis using multi-channel image fusion and deep learning in channel failure occasions on rolling bearings. Advanced Engineering Informatics, 68, 103631. https://doi.org/10.1016/j.aei.2025.103631
Raj, K. K., Kumar, S., & Kumar, R. R. (2024). Systematic Review of Bearing Component Failure: Strategies for Diagnosis and Prognosis in Rotating Machinery. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-024-09866-x
Rajabioun, R., Afshar, M., Atan, Ö., Mete, M., & Akin, B. (2023b). Classification of Distributed Bearing Faults using a Novel Sensory Board and Deep Learning Networks with Hybrid Inputs. IEEE Transactions on Energy Conversion, 39(2), 963–973. https://doi.org/10.1109/tec.2023.3338447
Sharma, A., Jigyasu, R., Mathew, L., & Chatterji, S. (2018). Bearing fault diagnosis using frequency domain features and artificial neural networks. In Smart innovation, systems and technologies (pp. 539–547). https://doi.org/10.1007/978-981-13-1747-7_52
Siddique, M. F., Saleem, F., Umar, M., Kim, C. H., & Kim, J. (2025c). A hybrid deep learning approach for bearing fault diagnosis using continuous Wavelet transform and Attention-Enhanced spatiotemporal feature extraction. Sensors, 25(9), 2712. https://doi.org/10.3390/s25092712
Singh, M. T. (2025, April 29). Graph-Based fault diagnosis for rotating machinery: adaptive segmentation and structural feature integration. arXiv.org. https://arxiv.org/abs/2504.20756
Soares, R. C., Silva, J. C., De Lucena, J. A., Filho, A. C. L., De Souza Ramos, J. G. G., & Brito, A. V. (2025). Integration of Bayesian optimization into hyperparameter tuning of the particle swarm optimization algorithm to enhance neural networks in bearing failure classification. Measurement, 242, 115829. https://doi.org/10.1016/j.measurement.2024.115829
Tama, B. A., Vania, M., Lee, S., & Lim, S. (2022). Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals. Artificial Intelligence Review, 56(5), 4667–4709. https://doi.org/10.1007/s10462-022-10293-3
Tang, J., Wu, J., Hu, B., & Qing, J. (2022b). Towards a Fault Diagnosis Method for Rolling Bearings with Time-Frequency Region-Based Convolutional Neural Network. Machines, 10(12), 1145. https://doi.org/10.3390/machines10121145
Wu, J., Chen, X. Y., Zhang, H., Xiong, L. D., Lei, H., & Deng, S. H. (2019). Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology, 17(1), 26–40. https://doi.org/10.11989/jest.1674-862x.80904120
Wu, Y., Dai, J., Yang, X., Shao, F., Gong, J., Zhang, P., & Liu, S. (2025). The fault diagnosis of rolling bearings based on FFT-SE-TCN-SVM. Actuators, 14(3), 152. https://doi.org/10.3390/act14030152
Xiao, Z., Xu, Y., & Cui, J. (2025b). VFQB: A novel deep learning model for rolling bearing fault diagnosis. Sensors, 25(9), 2678. https://doi.org/10.3390/s25092678
Yang, P., Zhang, B., & Zhao, J. (2025b). Efficient fault diagnosis in rolling bearings lightweight hybrid model. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-96285-z
Yu, Z., Zhang, L., & Kim, J. (2023b). The performance analysis of PSO-RESNET for the fault diagnosis of vibration signals based on the pipeline robot. Sensors, 23(9), 4289. https://doi.org/10.3390/s23094289
Zhang, B., Wang, W., & He, Y. (2025b). A hybrid approach combining deep learning and signal processing for bearing fault diagnosis under imbalanced samples and multiple operating conditions. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-98138-1
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Technical Papers