The following publications are possibly variants of this publication:
- DCNN With Explicable Training Guide and its Application to Fault Diagnosis of the Planetary GearboxesPeng Luo, Niaoqing Hu, Guoji Shen, Lun Zhang, Zhe Cheng. access, 8:122641-122653, 2020. [doi]
- Fault diagnosis for the planetary gearbox based on an improved LightGBMSiyuan Zhang, Yang Liu. caasafeproc 2021: 1-6 [doi]
- Multiscale Symbolic Diversity Entropy: A Novel Measurement Approach for Time-Series Analysis and Its Application in Fault Diagnosis of Planetary GearboxesYongbo Li, Shun Wang, Ni Li, Zichen Deng. tii, 18(2):1121-1131, 2022. [doi]
- The Optimized Deep Belief Networks With Improved Logistic Sigmoid Units and Their Application in Fault Diagnosis for Planetary Gearboxes of Wind TurbinesYi Qin, Xin Wang, Jingqiang Zou. tie, 66(5):3814-3824, 2019. [doi]
- Application of a Novel Improved Adaptive CYCBD Method in Gearbox Compound Fault DiagnosisHuer Sun, Fuwang Liang, Yutao Liu, Kexin Liu, Zhijian Wang, Tianyuan Zhang, Jiyang Zhu, Yang Zhao. access, 9:133835-133848, 2021. [doi]
- An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognitionZirui Wang, Jun Wang, Youren Wang. ijon, 310:213-222, 2018. [doi]
- Feature selection for fault level diagnosis of planetary gearboxesZhiliang Liu, Xiaomin Zhao, Ming J. Zuo, Hongbing Xu. adac, 8(4):377-401, 2014. [doi]
- Compound Fault Diagnosis of Planetary Gearbox Based on Improved LTSS-BoW Model and Capsule NetworkGuoyan Li, Liyu He, Yulin Ren, Xiong Li, Jingbin Zhang, Runjun Liu. sensors, 24(3):940, February 2024. [doi]