方位(导航)
小波
主题(音乐)
断层(地质)
小波包分解
模式识别(心理学)
人工智能
结构工程
计算机科学
工程类
小波变换
声学
地质学
地震学
物理
作者
Qiang Wang,Feiyun Xu,Tianchi Ma
标识
DOI:10.1177/10775463241229512
摘要
Bearing intelligent diagnosis based on signal processing has been a hot research topic. However, due to the different data distribution caused by the variable working loads, the model learned from source domain has poor performance in target domain. To solve this problem, a feature extraction method named Wavelet Packet Decomposition with Motif Patterns (WPDMP) is proposed. Firstly, multiscale signals are obtained using wavelet packet decomposition; then, the MP features of these multiscale signals and the original signal are extracted; finally, these MPs are combined as input vector of support vector classification (SVC) for fault identification. Compared with other methods, the proposed method has extraordinary superiority for unlabeled target domain fault diagnosis. In addition, the feature visualization results show that the proposed model can extract domain invariant features, so the proposed model has considerable research prospects.
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