针对故障诊断领域开源代码较少，小编整理搜集了一些开源代码与大家进行分享。本期分享的是北京交通大学何超博士提出的一种增强型半收缩小波权值初始化网络(EWSNet)。该论文是2023年发表在Journal of Manufacturing Systems上的，提供有该网络的开源代码，即插即用，且抗噪性能不错，因此这篇开源代码适合参考借鉴并在上面进行改进，适合具备一定的深度学习基础知识的学习者。
论文题目：Physics-informed interpretable wavelet weight initialization and balanced dynamic adaptive threshold for intelligent fault diagnosis of rolling bearings论文期刊：Journal of Manufacturing Systems论文时间：2023年作者：Chao He(a, b) and Hongmei Shi(a, b) and Jin Si(c) and Jianbo Li(a, b)机构：
a School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China
b Collaborative Innovation Center of Railway Traffic Safety, Beijing 100044, China
c Key Laboratory of Information System and Technology, Beijing Institute of Control and Electronic Technology, Beijing 100038, China
●A novel deep triple-stream network called EWSNet is proposed for fault diagnosis of rolling bearings under the condition of constant or sharp speed variation.●An enhanced wavelet convolution kernel is designed to improve the trainability, in which a scale smoothing factor is employed to acquire rational wavelet scales.●A plug-and-play and physics-informed wavelet weight initialization is proposed to construct an interpretable convolution kernel, which makes the diagnosis interpretable.●Balanced dynamic adaptive threshold is specially devised to improve the antinoise robustness of the model.●Normalization activation mapping is designed to visually reveal that Z-score can enhance the frequency-domain information of raw signals.
Normalization Activation Mapping
Data normalization can accelerate the process of convergence. Also, Z-score makes CNN get better accuracy. Unlike experimental methods, we notice that Z-score enhances frequency-domain information of signals so that CNN can learn these features better.FAM illustrates the frequency-domain information by utilizing the weights of the classification layer and extracted features, but it can not reveal the influence of normalization methods. Therefore in NAM, the weight of the correct label is 1.0, and the features are signals processed by the normalization methods and it can visualize which normalization method possesses more frequency-domain knowledge.
Example:
classCNNNet(nn.Module): def__init__(self, init_weights=False):super(CNNNet, self).__init__()self.conv1 = nn.Conv1d(1, 64, 60, padding=2)self.conv2 = nn.Conv1d(32, 32, 3, padding=1)self.conv3 = nn.Conv1d(16, 48, 3, padding=1)#self.sage = sage(channel=16, gap_size=1)self.conv4 = nn.Conv1d(24, 64, 3, padding=1)self.pool = nn.MaxPool2d(2)self.fc1 = nn.Linear(32*60, 512)self.fc2 = nn.Linear(512, 4)ifinit_weights:self._initialize_weights() def_initialize_weights(self):for m inself.modules():if isinstance(m, nn.Conv1d):if m.kernel_size == (60,): m.weight.data = fast(out_channels=64, kernel_size=60, eps=0.2, mode='sigmoid').forward() nn.init.constant_(m.bias.data, 0.0) defforward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x)))#x = x + self.sage(x) x = self.pool(F.relu(self.conv3(x))) x = self.pool(F.relu(self.conv4(x))) x = x.view(x.shape[0], -1) x = F.relu(self.fc1(x)) x = self.fc2(x)return x
Citation
@article{HE,
Ackowledgements
The authors are grateful for the supports of the Fundamental Research Funds for the Central Universities (Science and Technology Leading Talent Team Project) (2022JBXT005), and the National Natural Science Foundation of China (No.52272429).
References
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