情态动词
断层(地质)
计算机科学
信号(编程语言)
模式识别(心理学)
振动
非线性系统
人工智能
熵(时间箭头)
算法
数学
声学
物理
材料科学
程序设计语言
地质学
高分子化学
量子力学
地震学
作者
Shuihai Dou,Yanlin Liu,Yanping Du,Zhaohua Wang,Xiaomei Jia
标识
DOI:10.1007/s44196-023-00301-x
摘要
Abstract Aiming at the nonlinear and non-stationarity of gearbox fault signals and the confusion among different fault categories, a gear fault diagnosis method combining variational mode decomposition, reconstruction and ResNeXt is proposed in this paper. In this paper, parameter K of VMD is determined according to the changing trend of sample entropy (SE), K modal components are obtained after decomposition, and the effective modal components are extracted and reconstructed according to Pearson autocorrelation coefficient, so as to remove redundant information from the original signal. Then the reconstructed signal is transformed by time–frequency and output two-dimensional time–frequency information, which is used as the input of ResNeXt model to extract the characteristics of different faults. Moreover, the model performance is improved by changing the learning rate decline rate, and a fault diagnosis model with high precision and good stability is established.
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