This study introduces a predictive model for estimating the remaining useful life (RUL) of bearings, leveraging a deep learning approach based on Long Short-Term Memory (LSTM) networks and Continuous Wavelet Transform (CWT). The vibration signal data, which is essential for condition monitoring, were sourced from a well-established dataset. To enhance the model’s ability to capture time-frequency features, each vibration signal was processed using CWT, resulting in scalogram representations. These scalograms were then fed into the LSTM network to create an accurate RUL prediction model. The performance of the proposed LSTM-based deep learning model was thoroughly assessed by comparing it with three conventional artificial neural network (ANN) models, each trained using a different algorithm: Trainbr, Trainlm, and Trainscg. The results demonstrated that the LSTM model significantly outperformed the traditional ANN models, yielding a Root Mean Square Error (RMSE) of 0.18 and a Mean Absolute Percentage Error (MAPE) of 0.0103. In contrast, the three ANN models resulted in much higher average RMSE and MAPE values of 12.4377 and 1.5557, respectively. These findings confirm the superiority of the LSTM-based model for RUL estimation in bearing health monitoring and its potential for real-world industrial applications.
Lee, J., et al. (2014). 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
Jiang, W., et al. (2021). Application of deep learning in fault diagnosis of rotating machinery processes. Processes, 9(6), 919. https://doi.org/10.3390/pr9060919
Sun, Y., Feng, T., & Jin, Z. (2021). Review on vibration signal analysis of rotating machinery based on deep learning. Journal of Physics: Conference Series, IOP Publishing. https://doi.org/10.1088/1742-6596/1820/1/012034
Huang, N. E., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995. https://doi.org/10.1098/rspa.1998.0193
Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(01), 1-41. https://doi.org/10.1142/S1793536909000047
Liu, W., Cao, S., & Chen, Y. (2016). Applications of variational mode decomposition in seismic time-frequency analysis. Geophysics, 81(5), V365-V378. https://doi.org/10.1190/geo2015-0489.1
Lee, Y., et al. (2023). A quantitative diagnostic method of feature coordination for machine learning models with massive data from rotary machines. Expert Systems with Applications, 214, 119117. https://doi.org/10.1016/j.eswa.2022.119117
Seryasat, O. R., Honarvar, F., & Rahmani, A. (2010, October). Multi-fault diagnosis of ball bearing using FFT, wavelet energy entropy mean, and root mean square (RMS). In 2010 IEEE International Conference on Systems, Man, and Cybernetics (pp. 4295-4299). IEEE. https://doi.org/10.1109/ICSMC.2010.5642389
Seryasat, O. R., Honarvar, F., & Rahmani, A. (2010, October). Multi-fault diagnosis of ball bearing based on features extracted from time-domain and multi-class support vector machine (MSVM). In 2010 IEEE International Conference on Systems, Man, and Cybernetics (pp. 4300-4303). IEEE. https://doi.org/10.1109/ICSMC.2010.5642390
Seryasat, O. R., Habibi, M., Ghane, M., & Taherkhani, H. (2014). Fault detection of rolling bearings using discrete wavelet transform and neural network of SVM. Advances in Environmental Biology, 2175-2184.
Seryasat, O. R., Shoorehdeli, M. A., Honarvar, F., Rahmani, A., & Haddadnia, J. (2010, August). Notice of Retraction: Multi-fault diagnosis of ball bearing using intrinsic mode functions, Hilbert marginal spectrum, and multi-class support vector machine. In 2010 2nd International Conference on Mechanical and Electronics Engineering (Vol. 2, pp. V2-145). IEEE. https://doi.org/10.1109/ICMEE.2010.5558468
Seryasat, O. R., Zadeh, H. G., Ghane, M., Abooalizadeh, Z., Taherkhani, A., & Maleki, F. (2013). Fault diagnosis of ball-bearings using principal component analysis and support-vector machine. Life Science Journal, 10(1s), 393-397.
Qiu, H., et al. (2006). Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of Sound and Vibration, 289(4-5), 1066-1090. https://doi.org/10.1016/j.jsv.2005.03.007
Chakrabarti, C., Vishwanath, M., & Owens, R. M. (1996). Architectures for wavelet transforms: A survey. Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 14, 171-192. https://doi.org/10.1007/BF00925498
Bostanov, V. (2004). Feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram. IEEE Transactions on Biomedical Engineering, 51(6), 1057-1061. https://doi.org/10.1109/TBME.2004.826702
Cheng, J., Dong, L., & Lapata, M. (2016). Long short-term memory networks for machine reading. arXiv preprint arXiv:1601.06733. https://doi.org/10.18653/v1/D16-1053
Vijh, M., et al. (2020). Stock closing price prediction using machine learning techniques. Procedia Computer Science, 167, 599-606. https://doi.org/10.1016/j.procs.2020.03.326
Li, Y., et al. (2021). Stochastic fractal search-optimized multi-support vector regression for remaining useful life prediction of bearings. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 43, 1-18. https://doi.org/10.1007/s40430-021-03138-7
Mao, W., He, J., & Zuo, M. J. (2019). Predicting remaining useful life of rolling bearings based on deep feature representation and transfer learning. IEEE Transactions on Instrumentation and Measurement, 69(4), 1594-1608. https://doi.org/10.1109/TIM.2019.2917735
Kundu, P., Darpe, A. K., & Kulkarni, M. S. (2019). Weibull accelerated failure time regression model for remaining useful life prediction of bearings working under multiple operating conditions. Mechanical Systems and Signal Processing, 134, 106302. https://doi.org/10.1016/j.ymssp.2019.106302
Amjadian,P. (2024). Remaining Useful Life Estimation of Bearings Using Vibration Signal Processing Based on Continuous Wavelet Transform and LSTM Deep Learning Network. Transactions on Machine Intelligence, 7(1), 61-69. doi: 10.47176/TMI.2024.61
MLA
Amjadian,P. . "Remaining Useful Life Estimation of Bearings Using Vibration Signal Processing Based on Continuous Wavelet Transform and LSTM Deep Learning Network", Transactions on Machine Intelligence, 7, 1, 2024, 61-69. doi: 10.47176/TMI.2024.61
HARVARD
Amjadian P. (2024). 'Remaining Useful Life Estimation of Bearings Using Vibration Signal Processing Based on Continuous Wavelet Transform and LSTM Deep Learning Network', Transactions on Machine Intelligence, 7(1), pp. 61-69. doi: 10.47176/TMI.2024.61
CHICAGO
P. Amjadian, "Remaining Useful Life Estimation of Bearings Using Vibration Signal Processing Based on Continuous Wavelet Transform and LSTM Deep Learning Network," Transactions on Machine Intelligence, 7 1 (2024): 61-69, doi: 10.47176/TMI.2024.61
VANCOUVER
Amjadian P. Remaining Useful Life Estimation of Bearings Using Vibration Signal Processing Based on Continuous Wavelet Transform and LSTM Deep Learning Network. Trans. Mach. Intell., 2024; 7(1): 61-69. doi: 10.47176/TMI.2024.61
