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Lee, H. W. (2020). Study of a mechanical arm and intelligent robot. IEEE Access, PP(99), 1-1.Search in Google Scholar
Kobayashi, Y., Song, L., Masaru, T., Mizushima, Y., Wang, H., & Chen, P. (2018). Intelligent diagnosis method for multi-flaws of roller bearing by time-frequency waveform distribution and extreme learning machine. International journal of comadem, 21(4), 1-5.Search in Google Scholar
Yan, X., She, D., & Xu, Y. (2023). Deep order-wavelet convolutional variational autoencoder for fault identification of rolling bearing under fluctuating speed conditions. Expert Systems with Applications, 216, 119479-.Search in Google Scholar
Li, B., Yang, Y., Qin, C., Bai, X., & Wang, L. (2020). Improved random sampling consensus algorithm for vision navigation of intelligent harvester robot. Industrial Robot, ahead-of-print(ahead-of-print).Search in Google Scholar
Pan, T., Chen, J., Xie, J., Zhou, Z., & He, S. (2020). Deep feature generating network: a new method for intelligent fault detection of mechanical systems under class imbalance. IEEE Transactions on Industrial Informatics, PP(99), 1-1.Search in Google Scholar
Xu, H., Pan, H., Zheng, J., Liu, Q., & Tong, J. (2022). Dynamic penalty adaptive matrix machine for the intelligent detection of unbalanced faults in roller bearing. Knowledge-based systems(Jul.8), 247.Search in Google Scholar
Sha, Y., He, Z., Gutierrez, H., Du, J., Yang, W., & Lu, X. (2022). The intelligent detection method for flip chips using cbn-s-net algorithm with sam images. Journal of Manufacturing Processes.Search in Google Scholar
Gao, Q., Wu, X., Guo, J., Zhou, H., & Ruan, W. (2021). Machine-learning-based intelligent mechanical fault detection and diagnosis of wind turbines. Mathematical Problems in Engineering, 2021.Search in Google Scholar
Pan, H., Xu, H., & Zheng, J. (2022). A novel symplectic relevance matrix machine method for intelligent fault diagnosis of roller bearing. Expert Systems with Applications, 192, 116400-.Search in Google Scholar
Mohanty, S. P. S. (2020). Intelligent prediction of engine failure through computational of wear. Engineering failure analysis, 116(1).Search in Google Scholar
Xu, F., Jiang, Z. S., & Jiang, H. (2019). A hybrid method for detection and diagnosis of faulty roller bearings. Key Engineering Materials, 795.Search in Google Scholar
Meulman, E., Renart, J., Carreras, L., & Zurbitu, J. (2022). Analysis of mode i fracture toughness of adhesively bonded joints by a low friction roller wedge driven quasi-static test. Engineering Fracture Mechanics, 271.Search in Google Scholar
Nguyen, H. A. T., Le, T. H., & Bui, T. D. (2020). A deep wavelet sparse autoencoder method for online and automatic electrooculographical artifact removal. Neural Computing and Applications, 32(24), -.Search in Google Scholar
Sun, C., & Zhang, Y. (2018). Research on automatic early warning method for rail flaw based on intelligent identification and periodic detection. Tiedao Xuebao/Journal of the China Railway Society, 40(11), 140-146.Search in Google Scholar
Chuanhui, L., Xiaojing, C., Xiaoxiao, H., Qiang, Z., & Yong, D. (2022). Fault diagnosis analysis of variable working condition gearbox based on dwae and grunn combination model. Journal of Mechanical Transmission, 46(2), 155-159.Search in Google Scholar