样本熵
振动
计算机科学
模式识别(心理学)
断层(地质)
算法
数学
控制理论(社会学)
人工智能
熵(时间箭头)
声学
模式(计算机接口)
物理
控制(管理)
地震学
地质学
操作系统
量子力学
作者
Na Lü,Tingxin Zhou,Juyao Wei,Wanmai Yuan,R Q Li,Meng-Meng Li
标识
DOI:10.1088/1361-6501/ac3470
摘要
Abstract In recent years, the variational mode decomposition (VMD) method has been introduced for rotating machinery fault diagnosis. However, the results largely depend on its parameters. When an optimization algorithm is employed to optimize these parameters, the fitness function is critical. In this paper, a new fitness function, envelope sample entropy, is constructed. Based on this, a whale optimized VMD method is proposed for rotating machinery fault diagnosis. First, the vibration signals were decomposed by the optimized VMD method to obtain a series of intrinsic mode functions (IMFs), from which the IMFs containing the main information were selected. Then, features were extracted from the selected IMFs and their dimensions were reduced using the local tangent space alignment method. Finally, support vector machine was adopted for fault identification. Compared with related methods, the experiment results show that the proposed method obtains a higher fault recognition accuracy.
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