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面向边缘计算的一维加法神经网络架构设计针对传统卷积神经网络在无线传感器节点上计算资源受限的问题,本研究采用加法运算替代乘法卷积操作构建轻量化诊断模型。
加法神经网络通过线性滤波器组与输入信号的差值计算来提取特征,相比传统卷积层能够显著降低浮点运算次数和硬件功耗开销。
网络结构设计上采用多尺度特征提取策略,通过堆叠不同感受野的加法层捕获振动信号在时域和频域的多层次特征表征。
为增强模型的非线性表达能力,在加法层之间引入批归一化和ReLU激活函数,同时设计跳跃连接机制将浅层细粒度特征与深层语义特征进行融合,构建端到端的故障模式识别通路。
针对轴承故障振动信号的周期性和瞬态冲击特性,网络输入层采用滑动窗口分段策略对原始时序数据进行预处理,将连续振动信号转化为固定长度的样本序列,通过数据增强技术扩充训练集规模提升模型泛化性能。
输出层设计为多分类softmax层,能够同时识别正常状态、内圈故障、外圈故障和滚动体故障等多种工况模式。
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基于结构化稀疏约束的通道剪枝优化方法为进一步压缩模型参数量和计算复杂度使其适配嵌入式硬件平台,本研究提出结合稀疏正则化和交替优化的剪枝策略。
首先通过在损失函数中引入组稀疏惩罚项对加法层的滤波器通道施加结构化约束,促使训练过程中部分通道的权重参数趋向于零,从而识别出对诊断性能贡献较小的冗余特征通道。
采用近端梯度下降算法求解带约束的优化问题,将参数更新分解为梯度下降步和软阈值收缩步交替执行,在保证模型精度的前提下逐步增强权重矩阵的稀疏性。
在剪枝决策阶段,根据各通道权重的范数大小进行重要性排序,设置全局剪枝率阈值移除低重要性通道及其对应的批归一化参数。
为避免剪枝后精度骤降,采用分层渐进式剪枝策略,从网络浅层到深层逐步提高剪枝比例,并在每轮剪枝后进行微调训练恢复模型性能。
针对不同故障类别样本数量不均衡的问题,在剪枝过程中引入类别敏感的重要性评估机制,对稀缺故障类别相关的特征通道赋予更高保留优先级,确保压缩后的模型对各类故障均具有鲁棒的判别能力。
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分布式边缘诊断的嵌入式系统实现与通信优化将剪枝优化后的轻量化模型部署到无线传感器网络的终端节点,实现本地化实时故障诊断与按需数据传输。
嵌入式实现采用定点量化技术将浮点权重参数转换为低比特整数表示,通过查找表加速激活函数计算,并利用SIMD指令优化加法运算的并行执行效率。
传感器节点采集振动加速度信号后,在本地完成信号预处理、特征提取和模式识别全流程,仅当检测到异常工况或诊断置信度低于阈值时才向汇聚节点发送诊断结果和关键特征向量,相比原始数据全量传输方案可将通信开销降低两个数量级。
针对多节点协同监测场景,设计基于令牌传递的时分复用通信协议,避免节点间数据碰撞导致的重传能耗。
import numpy as np import torch import torch.nn as nn import torch.optim as optim from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler class AdderLayer(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride1, padding
: super(AdderLayer, self).__init__() self.in_channels in_channels self.out_channels out_channels self.kernel_size kernel_size self.stride stride self.padding padding self.weight nn.Parameter(torch.randn(out_channels, in_channels, kernel_size)) def forward(self, x): batch_size x.size(
in_len x.size(
out_len (in_len 2 * self.padding - self.kernel_size) // self.stride 1 x_unfold nn.functional.unfold(x.unsqueeze(
, (1, self.kernel_size), padding(0, self.padding), stride(1, self.stride)) x_unfold x_unfold.transpose(1,
weight_flat self.weight.view(self.out_channels, -
output -torch.cdist(x_unfold, weight_flat.t(), p
output output.transpose(1,
.view(batch_size, self.out_channels, out_len) return output class ResidualAdderBlock(nn.Module): def __init__(self, channels, kernel_size): super(ResidualAdderBlock, self).__init__() self.adder1 AdderLayer(channels, channels, kernel_size, paddingkernel_size//
self.bn1 nn.BatchNorm1d(channels) self.relu nn.ReLU(inplaceTrue) self.adder2 AdderLayer(channels, channels, kernel_size, paddingkernel_size//
self.bn2 nn.BatchNorm1d(channels) def forward(self, x): residual x out self.adder1(x) out self.bn1(out) out self.relu(out) out self.adder2(out) out self.bn2(out) out residual out self.relu(out) return out class BearingFaultDiagnosisNet(nn.Module): def __init__(self, input_channels1, num_classes
: super(BearingFaultDiagnosisNet, self).__init__() self.adder_in AdderLayer(input_channels, 32, kernel_size64, stride8, padding
self.bn_in nn.BatchNorm1d(
self.relu nn.ReLU(inplaceTrue) self.block1 ResidualAdderBlock(32,
self.block2 ResidualAdderBlock(32,
self.adder_mid AdderLayer(32, 64, kernel_size8, stride4, padding
self.bn_mid nn.BatchNorm1d(
self.block3 ResidualAdderBlock(64,
self.global_pool nn.AdaptiveAvgPool1d(
self.fc nn.Linear(64, num_classes) def forward(self, x): x self.adder_in(x) x self.bn_in(x) x self.relu(x) x self.block1(x) x self.block2(x) x self.adder_mid(x) x self.bn_mid(x) x self.relu(x) x self.block3(x) x self.global_pool(x) x x.view(x.size(
, -
x self.fc(x) return x def channel_pruning(model, prune_ratio
0.
: pruned_model BearingFaultDiagnosisNet() pruned_model.load_state_dict(model.state_dict()) for name, module in pruned_model.named_modules(): if isinstance(module, AdderLayer): weight module.weight.data num_channels weight.size(
num_keep int(num_channels * (1 - prune_ratio)) channel_norm torch.norm(weight.view(num_channels, -
, p2, dim
_, indices torch.topk(channel_norm, num_keep) indices indices.sort()[0] module.weight nn.Parameter(weight[indices]) module.out_channels num_keep return pruned_model def train_model(model, train_loader, epochs
: criterion nn.CrossEntropyLoss() optimizer optim.Adam(model.parameters(), lr
001, weight_decay1e-
scheduler optim.lr_scheduler.StepLR(optimizer, step_size15, gamma
0.
model.train() for epoch in range(epochs): total_loss 0 correct 0 total 0 for inputs, labels in train_loader: optimizer.zero_grad() outputs model(inputs) loss criterion(outputs, labels) l1_reg sum(torch.norm(p,
for p in model.parameters()) loss loss 1e-5 * l1_reg loss.backward() optimizer.step() total_loss loss.item() _, predicted outputs.max(
total labels.size(
correct predicted.eq(labels).sum().item() scheduler.step() accuracy
1
0 * correct / total print(fEpoch {epoch1}: Loss{total_loss/len(train_loader):.4f}, Acc{accuracy:.2f}%) return model def generate_synthetic_data(num_samples5000, seq_length
: X np.random.randn(num_samples, 1, seq_length) y np.random.randint(0, 10, num_samples) for i in range(num_samples): fault_type y[i] if fault_type 0: freq 20 fault_type * 10 amplitude
5 fault_type *
1 t np.linspace(0, 1, seq_length) X[i, 0, :] amplitude * np.sin(2 * np.pi * freq * t) if fault_type % 3 0: impulse_locs np.random.randint(0, seq_length, size
X[i, 0, impulse_locs] np.random.randn(
*
0 return X.astype(np.float
, y X_data, y_data generate_synthetic_data() X_train, X_test, y_train, y_test train_test_split(X_data, y_data, test_size
0.
scaler StandardScaler() X_train_scaled scaler.fit_transform(X_train.reshape(X_train.shape[0], -
) X_test_scaled scaler.transform(X_test.reshape(X_test.shape[0], -
) X_train_scaled X_train_scaled.reshape(X_train.shape) X_test_scaled X_test_scaled.reshape(X_test.shape) train_dataset torch.utils.data.TensorDataset( torch.FloatTensor(X_train_scaled), torch.LongTensor(y_train) ) train_loader torch.utils.data.DataLoader(train_dataset, batch_size64, shuffleTrue) model BearingFaultDiagnosisNet(input_channels1, num_classes
trained_model train_model(model, train_loader, epochs
pruned_model channel_pruning(trained_model, prune_ratio
0.
test_dataset torch.utils.data.TensorDataset( torch.FloatTensor(X_test_scaled), torch.LongTensor(y_test) ) test_loader torch.utils.data.DataLoader(test_dataset, batch_size
pruned_model.eval() correct 0 total 0 with torch.no_grad(): for inputs, labels in test_loader: outputs pruned_model(inputs) _, predicted outputs.max(
total labels.size(
correct predicted.eq(labels).sum().item() print(fTest Accuracy: {
1