计算机科学
卷积神经网络
人工智能
方位(导航)
格拉米安矩阵
残余物
瓶颈
断层(地质)
领域(数学)
架空(工程)
深度学习
卷积(计算机科学)
模式识别(心理学)
数据挖掘
人工神经网络
计算机视觉
算法
嵌入式系统
数学
地质学
物理
操作系统
量子力学
特征向量
地震学
纯数学
作者
Jialiang Cui,Qianwen Zhong,Shubin Zheng,Lele Peng,Jing Wen
出处
期刊:Machines
[MDPI AG]
日期:2022-04-17
卷期号:10 (4): 282-282
被引量:10
标识
DOI:10.3390/machines10040282
摘要
The key to ensuring rotating machinery’s safe and reliable operation is efficient and accurate faults diagnosis. Intelligent fault diagnosis technology based on deep learning (DL) has gained increasing attention. A critical challenge is how to embed the characteristics of time series into DL to obtain stable features that correlate with equipment conditions. This study proposes a lightweight rolling bearing fault diagnosis method based on Gramian angular field (GAF) and coordinated attention (CA) to improve rolling bearing recognition performance and diagnosis efficiency. Firstly, the time domain signal is encoded into GAF images after downsampling and segmentation. This method retains the temporal relation of the time series and provides valuable features for DL. Secondly, a lightweight convolution neural network (CNN) model is constructed through depthwise separable convolution, inverse residual block, and linear bottleneck layer to learn advanced features. After that, CA is employed to capture the long-range dependencies and identify the precise position information of the GAF images with nearly no additional computational overhead. The proposed method is tested and evaluated by CWRU bearing dataset and experimental dataset. The results demonstrate that the CNN based on GAF and CA (GAF-CA-CNN) model can effectively reduce the calculation overhead of the model and achieve high diagnostic accuracy.
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