论文著作情况： [1] Xiong, S., He, S., Xuan, J., Xia, Q. & Shi, T. Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery. JVC/Journal of Vibration and Control (2020). (JCR Q1. Impact factor: 3.095.) [2] Xiong, S. Zhou, H., He, S., Zhang, L., & Shi, T.. A novel end-to-end fault diagnosis approach for rolling bearings by integrating wavelet packet transform into convolutional neural network structures. Sensors (Switzerland) 20, 1–26 (2020). (JCR Q1. Impact factor: 3.576.) [3] Xiong, S., Zhou, H., He, S., Zhang, L., & Shi, T. Fault diagnosis of rolling bearing based on the Wavelet Packet Transform and Deep Residual Network with lightweight multi branch structure. Measurement Science and Technology (2021). (JCR Q2. Impact factor: 2.046) [4] Xiong, S. & Shi, T. Deep residual network for enhanced fault diagnosis of rotating machinery. Journal of Physics: Conference Series (JPCS). (2020). [5] 史铁林,轩建平,熊守从. 一种改进的机床健康状态评定方法及数控机床.（专利号：ZL 2018 1 1502568.8）
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