e-ISSN 2231-8542
ISSN 1511-3701
J
Pertanika Journal of Tropical Agricultural Science, Volume J, Issue J, January J
Keywords: J
Published on: J
J
Aggab, T., Vrignat, P., Avila, M., & Kratz, F. (2022). Remaining useful life estimation based on the joint use of an observer and a hidden Markov model. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 236(5), 676–695. https://doi.org/10.1177/1748006X211044343
Cao, Y., Jia, M., Ding, P., & Ding, Y. (2021). Transfer learning for remaining useful life prediction of multi-conditions bearings based on bidirectional-GRU network. Measurement, 178, Article 109287. https://doi.org/10.1016/j.measurement.2021.109287
Chen, Y., Duan, W., Ding, Z., & Li, Y. (2022). Battery life prediction based on a hybrid support vector regression model. Frontiers in Energy Research, 10, Article 899804.
Cui, L., & Su, Y. (2021). Contact fatigue life prediction of rolling bearing considering machined surface integrity. Industrial Lubrication and Tribology, 74(1), 73–80. https://doi.org/10.1108/ILT-08-2021-0345
Gamanayake, C., Qin, Y., Yuen, C., Jayasinghe, L., Tan, D.-E., & Low, J. (2023). A hybrid deep learning model-based remaining useful life estimation for reed relay with degradation pattern clustering. IEEE Transactions on Industrial Informatics, 19(6), 7401–7413. https://doi.org/10.1109/TII.2022.3210250
Gu, M. Y., Ge, J. Q., & Li, Z. N. (2023). Improved similarity-based residual life prediction method based on grey Markov model. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 45(6), Article 294. https://doi.org/10.1007/s40430-023-04176-z
Hong, S., Zhou, Z., Zio, E., & Wang, W. (2014). An adaptive method for health trend prediction of rotating bearings. Digital Signal Processing, 35, 117–123. https://doi.org/10.1016/j.dsp.2014.08.006
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2019). Squeeze-and-Excitation Networks (arXiv:1709.01507). arXiv. https://doi.org/10.48550/arXiv.1709.01507
Juodelyte, D., Cheplygina, V., Graversen, T., & Bonnet, P. (2022). Predicting bearings degradation stages for predictive maintenance in the pharmaceutical industry. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 3107-3115). ACM Publishing. https://doi.org/10.1145/3534678.3539057
Kou, L., Chu, B., Chen, Y., & Qin, Y. (2022). An automatic partition time-varying markov model for reliability evaluation. Applied Sciences, 12(12), Article 12. https://doi.org/10.3390/app12125933
Li, H. F., Wei, J. L., Li, S. H., Liu, Y. Q., Gu, X. H., Liu, Z. C., & Yang, S. P. (2023). Fatigue life prediction of high-speed train bearings based on the generalized linear cumulative damage theory. Fatigue & Fracture of Engineering Materials & Structures, 46(6), 2112–2120. https://doi.org/10.1111/ffe.13984
Li, X., Zhang, W., & Ding, Q. (2019). Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliability Engineering & System Safety, 182, 208–218. https://doi.org/10.1016/j.ress.2018.11.011
Liao, D., Yin, M., Luo, H., Li, J., & Wu, N. (2022). Machine vision system based on a coupled image segmentation algorithm for surface-defect detection of a Si3N4 bearing roller. JOSA A, 39(4), 571–579. https://doi.org/10.1364/JOSAA.449088
Mylonas, C., & Chatzi, E. (2020). Remaining Useful Life Estimation Under Uncertainty with Causal GraphNets (arXiv:2011.11740; Version 1). arXiv. http://arxiv.org/abs/2011.11740
Qiu, S., Cui, X., Ping, Z., Shan, N., Li, Z., Bao, X., & Xu, X. (2023). Deep learning techniques in intelligent fault diagnosis and prognosis for industrial systems: A review. Sensors, 23(3), Article 3. https://doi.org/10.3390/s23031305
Ren, L., Sun, Y., Cui, J., & Zhang, L. (2018). Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems, 48, 71–77. https://doi.org/10.1016/j.jmsy.2018.04.008
Ruan, D., Han, J., Yan, J., & Gühmann, C. (2023). Light convolutional neural network by neural architecture search and model pruning for bearing fault diagnosis and remaining useful life prediction. Scientific Reports, 13(1), Article 1. https://doi.org/10.1038/s41598-023-31532-9
Sanakkayala, D. C., Varadarajan, V., Kumar, N., Karan, Soni, G., Kamat, P., Kumar, S., Patil, S., & Kotecha, K. (2022). Explainable AI for bearing fault prognosis using deep learning techniques. Micromachines, 13(9), Article 9. https://doi.org/10.3390/mi13091471
Sutrisno, E., Oh, H., Vasan, A. S. S., & Pecht, M. (2012). Estimation of remaining useful life of ball bearings using data driven methodologies. In 2012 IEEE Conference on Prognostics and Health Management (pp. 1-7). IEEE Publishing. https://doi.org/10.1109/ICPHM.2012.6299548
Wang, B., Pan, H., & Yang, W. (2017). Robust bearing degradation assessment method based on improved CVA. IET Science, Measurement & Technology, 11(5), 637-645. https://doi.org/10.1049/iet-smt.2016.0391
Wang, C., Jiang, W., Yang, X., & Zhang, S. (2021). RUL prediction of rolling bearings based on a DCAE and CNN. Applied Sciences, 11(23), Article 23. https://doi.org/10.3390/app112311516
Wang, F., Liu, X., Deng, G., Yu, X., Li, H., & Han, Q. (2019). Remaining life prediction method for rolling bearing based on the long short-term memory network. Neural Processing Letters, 50(3), 2437–2454. https://doi.org/10.1007/s11063-019-10016-w
Wang, Y., Deng, L., Zheng, L., & Gao, R. X. (2021). Temporal convolutional network with soft thresholding and attention mechanism for machinery prognostics. Journal of Manufacturing Systems, 60, 512–526. https://doi.org/10.1016/j.jmsy.2021.07.008
Xia, T., & Xi, L. (2019). Manufacturing paradigm-oriented PHM methodologies for cyber-physical systems. Journal of Intelligent Manufacturing, 30(4), 1659–1672. https://doi.org/10.1007/s10845-017-1342-2
Zhang, C., Pan, L., Wang, S., Wang, X., & Tomovic, M. (2018). An accelerated life test model for solid lubricated bearings used in space based on time-varying dependence analysis of different failure modes. Acta Astronautica, 152, 352–359. https://doi.org/10.1016/j.actaastro.2018.08.027
Zhou, Q., Shen, H., Zhao, J., Liu, X., & Xiong, X. (2019). Degradation state recognition of rolling bearing based on K-means and CNN algorithm. Shock and Vibration, 2019, Article e8471732. https://doi.org/10.1155/2019/8471732
Zhu, G., Zhu, Z., Xiang, L., Hu, A., & Xu, Y. (2023). Prediction of bearing remaining useful life based on DACN-ConvLSTM model. Measurement, 211, Article 112600. https://doi.org/10.1016/j.measurement.2023.112600
Zhu, J., Nostrand, T., Spiegel, C., & Morton, B. (2014). Survey of condition indicators for condition monitoring systems. Annual Conference of the PHM Society, 6(1), Article 1. https://doi.org/10.36001/phmconf.2014.v6i1.2514
ISSN 1511-3701
e-ISSN 2231-8542