M. Pecht, “Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes,” IEEE Transactions on Industrial Electronics, vol. 65, no. 5, pp. 4290–4300, 2018. 2. M. Zhao, M. Kang, B. Tang, and M. Pecht, "Multiple Wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis," IEEE Transactions on Industrial Electronics, vol. 66, no. 6, pp. 4696-4706, 2019. 3. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. CVPR, Seattle, WA, USA, Jun. 27–30, 2016, pp. 770–778. 4. K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in Computer Vision—ECCV 2016 (Lecture Notes in Computer Science 9908), B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds., Cham, Switzerland: Springer, 2016, pp. 630–645. 5. D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995. 6. J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proc. IEEE Conf. CVPR, Salt Lake City, UT, USA, Jun. 18–23, 2018, pp. 7132–7141. 7. K. Isogawa, T. Ida, T. Shiodera, and T. Takeguchi, “Deep shrinkage convolutional neural network for adaptive noise reduction,” IEEE Signal Processing Letters, vol. 25, no. 2, pp. 224–228, 2018. M. Zhao, S. Zhong, X. Fu, B. Tang, and M. Pecht, “Deep Residual Shrinkage Networks for Fault Diagnosis,” IEEE Transactions on Industrial Informatics, 2019, to be published. 15