峰度
计算机科学
模式识别(心理学)
一般化
卷积神经网络
算法
交叉熵
融合
断层(地质)
熵(时间箭头)
传感器融合
人工智能
数学
数学分析
语言学
统计
哲学
物理
量子力学
地震学
地质学
作者
Hongwei Wang,Wenlei Sun,Li He,Jianxing Zhou
出处
期刊:Entropy
[Multidisciplinary Digital Publishing Institute]
日期:2022-04-19
卷期号:24 (5): 573-573
被引量:15
摘要
To satisfy the requirements of the end-to-end fault diagnosis of rolling bearings, a hybrid model, based on optimal SWD and 1D-CNN, with the layer of multi-sensor data fusion, is proposed in this paper. Firstly, the BAS optimal algorithm is adopted to obtain the optimal parameters of SWD. After that, the raw signals from different channels of sensors are segmented and preprocessed by the optimal SWD, whose name is BAS-SWD. By which, the sensitive OCs with higher values of spectrum kurtosis are extracted from the raw signals. Subsequently, the improved 1D-CNN model based on VGG-16 is constructed, and the decomposed signals from different channels are fed into the independent convolutional blocks in the model; then, the features extracted from the input signals are fused in the fusion layer. Finally, the fused features are processed by the fully connected layers, and the probability of classification is calculated by the cross-entropy loss function. The result of comparative experiments, based on different datasets, indicates that the proposed model is accurate, effective, and has a good generalization ability.
科研通智能强力驱动
Strongly Powered by AbleSci AI