Chen, X., Yang, R. & Xue, Y. Deep transfer learning for bearing fault diagnosis: A systematic review since 2016. IEEE Trans. 72, 1–21. https://doi.org/10.1109/TIM.2023.3244237 (2023).
Google Scholar
Salunkhe, V. G. et al. A novel incipient fault detection technique for roller bearing using deep independent component analysis and variational modal decomposition. ASME J. Tribol. 145, 7. https://doi.org/10.1115/1.4056899 (2023).
Google Scholar
Zhou, X. et al. Fault diagnosis method of rolling bearing based on improved VMD spectrogram and FCM. Mach. Tools Hydraul. 51, 206–211. https://doi.org/10.1007/s11042-020-09534-w (2023).
Google Scholar
Cheng, X. et al. Gearbox fault diagnosis method based on lightweight channel attention mechanism and transfer learning. Sci. Rep. 14, 743. https://doi.org/10.1038/s41598-023-50826-6 (2024).
Google Scholar
Jin, X. H., Zhao, M. B., Chow, T. W. S. & Pecht, M. Motor bearing fault diagnosis using trace ratio linear discriminant analysis. 61, 2441–2451. https://doi.org/10.1109/TIE.2013.2273471 (2014).
Salunkhe, V. G. & Desavale, R. G. An intelligent prediction for detecting bearing vibration characteristics using a machine learning model. ASME J. Nondestructive Evaluation. 4, 3. https://doi.org/10.1115/1.4049938 (2021).
Google Scholar
Wang, Q., Hu, S. & Wang, X. Detection of incipient rotor unbalance fault based on the RIME-VMD and modified-WKN. Sci. Rep. 14, 4683. https://doi.org/10.1038/s41598-024-54984-z (2024).
Google Scholar
Mao, M. et al. Application of FCEEMD-TSMFDE and adaptive catboost in fault diagnosis of complex variable condition bearings. Sci. Rep. 14, 30448. https://doi.org/10.1038/s41598-024-78845-x (2024).
Google Scholar
Yan, R. Q., Shang, Z. G., Xu, H. & Wen, J. C. Wavelet transform for rotary machine fault diagnosis:10 years revisited. Mech. Syst. Signal. Proc. 00, 110545. https://doi.org/10.1016/j.ymssp.2023.110545 (2023).
Google Scholar
Randall, R. B. & Antoni, J. Why EMD and similar decompositions are of little benefit for bearing diagnostics. Mech. Syst. Signal. Proc. 192, 110207. https://doi.org/10.1016/j.ymssp.2023.110545 (2023).
Google Scholar
Cheng, Y. & Zou, D. Complementary ensemble local means decomposition method and its application to rolling element bearings fault diagnosis. Proc. Inst. Mech. Eng. O 233, 868–880 (2019).
Wang, Z., Yang, J. & Guo, Y. Unknown fault feature extraction of rolling bearings under variable speed conditions based on statistical complexity measures. Mech. Syst. Signal. Proc. 72, 108964. https://doi.org/10.1016/j.ymssp.2022.108964 (2022).
Google Scholar
Dragomiretskiy, K. & Zosso, D. Variational mode decomposition. IEEE Trans. Signal. Process. 62, 531–544. https://doi.org/10.1109/TSP.2013.2288675 (2013).
Google Scholar
Zhang, S., Wang, Y. & He, S. Bearing fault diagnosis based on variational mode decomposition and total variation denoising. Meas. Sci. Technol. 27, 075101. https://doi.org/10.1088/0957-0233/27/7/075101 (2016).
Google Scholar
Li, K., Su, L., Wu, J. & Wang, H. A. Rolling bearing fault diagnosis method based on variational mode decomposition and an improved kernel extreme learning machine. Appl. Sci. 7, 1004. https://doi.org/10.3390/app7101004 (2017).
Google Scholar
Yuan, Y. et al. Noise reduction and feature enhancement of hob vibration signal based on parameter adaptive VMD and autocorrelation analysis. Meas. Sci. Technol. 33, 125116. https://doi.org/10.1088/1361-6501/ac8e23 (2022).
Google Scholar
Xing, Y. & Jian, R. Features method for selecting VMD parameters based on spectrum without modal overlap. J. Phys. Conf. Ser. 1605, 012002. https://doi.org/10.1088/1742-6596/1605/1/012002 (2020).
Google Scholar
Zhong, X., Xia, T. & Mei, Q. An effective centre frequency selection scheme based on variational mode extraction and its application to gear fault diagnosis. Insight-Non-Destructive Test. Condition Monit. 64, 155–163. https://doi.org/10.1784/insi.2022.64.3.155 (2022).
Google Scholar
Li, H., Liu, T., Wu, X. & Chen, Q. An optimized VMD method and its applications in bearing fault diagnosis. Meas 166, 108185. https://doi.org/10.1016/j.measurement.2020.108 (2020).
Google Scholar
Zhang, X., Miao, Q., Zhang, H. & Wang, L. A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery. Mech. Syst. Signal. Proc. 108, 58–72. https://doi.org/10.1016/j.ymssp.2017.11.029 (2018).
Google Scholar
Jin, Z., He, D. & Wei, Z. Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN. Eng. Appl. Artif. Intell. 110, 104713. https://doi.org/10.1016/j.engappai.2022.104713 (2022).
Google Scholar
Wang, X. et al. Fault diagnosis method of rolling bearing based on SSA-VMD and RCMDE. Sci. Rep. 14, 30637. https://doi.org/10.1038/s41598-024-81262-9 (2024).
Google Scholar
Tang, J., Liu, G. & Pan, Q. A review on representative swarm intelligence algorithms for solving optimization problems: applications and trends. IEEE-CAA J. AUTOMATIC. 8 (10), 1627–1643. https://doi.org/10.1109/JAS.2021.1004129 (2021).
Google Scholar
Xue, J. & Shen, B. Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. J. Supercomput. 79, 7305–7336. https://doi.org/10.1007/s11227-022-04959-6 (2023).
Google Scholar
Kong, W. et al. PID control algorithm based on multistrate-gy enhanced Dung beetle optimizer and back propagation neural network for DC motor control. Sci. Rep. 14, 28276. https://doi.org/10.1038/s41598-024-79653-z (2024).
Google Scholar
Tang, X., He, Z. & Jia, C. Multi-strategy cooperative enhancement Dung beetle optimizer and its application in obstacle avoidance navigation. Sci. Rep. 14, 28041. https://doi.org/10.1038/s41598-024-79420-0 (2024).
Google Scholar
Li, X. et al. A review on convolutional neural network in rolling bearing fault diagnosis. Meas. Sci. Technol. 35, 1. https://doi.org/10.1088/1361-6501/ad356e (2024).
Google Scholar
Salunkhe, V. G. et al. Vibration dynamic analysis of the bearing parameters in steam turbine bearing systems in sugar refinery. ASME J. Tribol. 148, 13. https://doi.org/10.1115/1.4068559 (2025).
Google Scholar
Ruan, D. W., Han, J. Z., Yan, J. P. & Guehmann, C. Light convolutional neural network by neural architecture search and model pruning for bearing fault diagnosis and remaining useful life prediction. Sci. Rep. 13, 1. https://doi.org/10.1038/s41598-023-31532 (2023).
Google Scholar
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444. https://doi.org/10.1038/nature14539 (2015).
Google Scholar
Choudhary, A., Mian, T. & Fatima, S. Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images. Meas 176, 109196. https://doi.org/10.1016/j.measurement.2021.109196 (2021).
Google Scholar
Tang, G., Zhou, Y., Wang, H. & Li, G. Prediction of bearing performance degradation with bottleneck feature based on LSTM network. IEEE Int. Inst. Meas. Tec. Conf. 1–6. https://doi.org/10.1109/I2MTC.2018.8409564 (2018).
Guo, Y., Mao, J. & Zhao, M. Rolling bearing fault diagnosis method based on attention CNN and BiLSTM network. Neural Process. Lett. 55, 3377–3410. https://doi.org/10.1007/s11063-022-11013-2 (2023).
Google Scholar
Zhang, Y. et al. Attention activation network for bearing fault diagnosis under various noise environments. Sci. Rep. 15, 977. https://doi.org/10.1038/s41598-025-85275-w (2025).
Google Scholar
Salunkhe, V. G. et al. Rolling element bearing fault diagnosis by the implementation of Elman neural networks with long Short-Term memory strategy. ASME J. Tribol. 147, 8. https://doi.org/10.1115/1.4067382 (2025).
Google Scholar
Saeed, A., Khan, A. & Akram, M. Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments. Sci. Rep. 15, 1114. https://doi.org/10.1038/s41598-024-79151-2 (2025).
Google Scholar
Chen, X., Zhang, B. & Gao, D. Bearing fault diagnosis base on multi-scale CNN and LSTM model. J. Intell. Manuf. 32, 971–987. https://doi.org/10.1007/s10845-020-01600-2 (2021).
Google Scholar
Nacer, S. M. et al. A novel method for bearing fault diagnosis based on BiLSTM neural networks. Int. J. Adv. Manuf. Technol. 125, 1477–1492. https://doi.org/10.1007/s00170-022-10792-1 (2023).
Google Scholar
Salunkhe, V. G. et al. Unbalance bearing fault identification using highly accurate Hilbert–Huang transform approach. J. Nondestr Eval Diag. 6, 3. https://doi.org/10.1115/1.4062929 (2023).
Google Scholar
Yıldız, B. S., Kumar, S., Pholdee, N. & Yildiz, A. R. A new chaotic lévy flight distribution optimization algorithm for solving constrained engineering problems. Expert Syst. Appl. 39, 12992. https://doi.org/10.1111/exsy.12992 (2022).
Google Scholar
Tanyildizi, E. & Demir, G. Golden sine algorithm: A novel Math-Inspired algorithm. Adv. Electr. Comput. Eng. 17, 71–78. https://doi.org/10.4316/AECE.2017.02010 (2017).
Google Scholar
Seyedali, M. SCA: A sine cosine algorithm for solving optimization problems. KNOWL-BASED SYST. 96, 120–133. https://doi.org/10.1016/j.knosys.2015.12.022 (2016).
Google Scholar
Ghasemi, M., Zare, M. & Trojovský, P. Optimization based on the smart behavior of plants with its engineering applications: Ivy algorithm. Knowl.-Based Syst. 295, 111850. https://doi.org/10.1016/j.knosys.2024.111850 (2024).
Google Scholar
Amiri, M. H. et al. Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm. Sci. Rep. 14, 5032. https://doi.org/10.1038/s41598-024-54910-3 (2024).
Google Scholar
Xin, Y., Yong, L. & Guang, L. Evolutionary programming made faster. 3, 102. https://doi.org/10.1109/4235.771163 (1999).
Wang, Y. H. et al. Degradation trend prediction of hydropower units based on a comprehensive deterioration index and LSTM. Energies 15, 6273. https://doi.org/10.3390/en15176273 (2022).
Jiang, Z. et al. A fault detection of aero-engine rolling bearings based on CNN-BiLSTM network integrated cross-attention. Meas. Sci. Technol. 35, 12 (2014).
Dao, F. et al. Wear fault diagnosis in hydro-turbine via the incorporation of the IWSO algorithm optimized CNN-LSTM neural network. Sci. Rep. 14, 25278. https://doi.org/10.1038/s41598-024-77251-7 (2024).
Google Scholar
Li, X. et al. An improved sparrow search algorithm and CNN-BiLSTM neural network for predicting sea level height. Sci. Rep. 14, 4560. https://doi.org/10.1038/s41598-024-55266-4 (2024).
Google Scholar
Salunkhe, V. G. et al. An integrated dimension theory and modulation signal bispectrum technique for analyzing bearing fault in industrial fibrizer. J. Nondestr Eval Diag. 7, 3. https://doi.org/10.1115/1.4065545 (2024).
Google Scholar
Song, Q. et al. Fault diagnosis of HVCB via the Subtraction average based optimizer algorithm optimized multi channel CNN-SABO-SVM network. Sci. Rep. 14, 29507. https://doi.org/10.1038/s41598-024-80954-6 (2024).
Google Scholar
Xiao, Q. et al. Feature decoupling integrated domain generalization network for bearing fault diagnosis under unknown operating conditions. Sci. Rep. 14, 30848. https://doi.org/10.1038/s41598-024-81489-6 (2024).
Google Scholar
Zhao, C., Zio, E. & Shen, W. Domain generalization for Cross-Domain fault diagnosis: an Application-oriented perspective and a benchmark study. Reliab. Eng. Syst. Safe. 245, 109964. https://doi.org/10.1016/j.ress.2024.109964 (2024).
Google Scholar
Jagadeesha, T. et al. Investigation of Crack Detection Technique in a Rotating Shaft by Using Vibration Measurement 631–645. https://doi.org/10.1007/978-981-15-4739-3_54 (AIME, 2021).
Song, J. et al. Reliability analysis of gear-bearing drive systems considering gear manufacturing and installation errors. Sci. Rep. 15, 23301. https://doi.org/10.1038/s41598-025-06446-3 (2025).
Google Scholar
Salunkhe, V. G., Desavale, R. G. & Jagadeesha, T. A numerical model for fault diagnosis in deep groove ball bearing using dimension theory. Mater. Today Proc. 47, 3077–3084. https://doi.org/10.1016/j.matpr.2021.06.072 (2021).