Boosting(机器学习)
计算机科学
能量操作员
模式识别(心理学)
振动
断层(地质)
噪音(视频)
特征提取
算法
滚动轴承
能量(信号处理)
降噪
人工智能
数学
声学
物理
统计
地震学
图像(数学)
地质学
作者
Yan Wang,Jiabo Li,Penghui Bu,Min Ye
标识
DOI:10.1088/1361-6501/ad0769
摘要
Abstract The incipient fault features of rolling element bearings (REBs) are easily overwhelmed by environmental noise and vibration interference. Therefore, this paper proposes a novel fault feature extraction method for REBs based on a SOSO (Strengthen-Operate denoising-Subtract-Strengthen) boosting technique. Firstly, an improved fast non-local mean filtering (IFNLM) algorithm is proposed by improving the similarity measure and kernel function while reducing the amount of weight calculation based on distance symmetry. Secondly, a SOSO_IFNLM boosting filtering structure is constructed to reduce the noise of the original vibration signal and enhance the early faint fault pulse. Finally, a k-value improved symmetric higher-order frequency-weighted energy operator (k-SHFWEO) is proposed to detect the bearing fault features from denoised signals. The effectiveness and feasibility of the proposed SOSO_IFNLM-k-SHFWEO method are numerically and experimentally investigated. The results demonstrate that the proposed method has better fault feature extraction capability for early weak faults of REBs and higher efficiency compared to other popular methods.
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