Vibration Analysis of an Industrial Motor with Autoencoder for Predictive Maintenance
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-19496-2_19
Reference18 articles.
1. Lemes, L.C., Hvam, L.: Maintenance costs in the process industry: a literature review. IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1481–1485 (2019). https://doi.org/10.1109/IEEM44572.2019.8978559
2. Mobley, R.K., et al.: An Introduction to Predictive Maintenance Second Edition (2002)
3. Shao, H., Jiang, H., Zhao, H., Wang, F.: A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mech. Syst. Signal Process. 95, 187–204 (2017). https://doi.org/10.1016/j.ymssp.2017.03.034
4. Cheng, Z., Sun, H., Takeuchi, M., Katto, J.: Deep convolutional autoencoder-based lossy image compression. In: 2018 Picture Coding Symposium PCS 2018 – Proceedings, pp. 253–257 (2018). https://doi.org/10.1109/PCS.2018.8456308
5. Azarang, A., Manoochehri, H.E., Kehtarnavaz, N.: Convolutional autoencoder-based multispectral image fusion. IEEE Access. 7, 35673–35683 (2019). https://doi.org/10.1109/ACCESS.2019.2905511
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