齿轮典型故障精确建模与智能诊断【附代码】
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（1）齿轮齿根裂纹与断齿精确动力学建模：
基于有限元方法构建单对齿轮三维实体模型，在齿根圆处预制不同长度（1mm、2mm、3mm）和不同角度（30°、45°、60°）的裂纹，通过瞬态动力学求解器计算啮合过程中的应力分布及振动加速度响应。仿真结果显示，裂纹深度超过2mm时齿根应力集中系数达到3.2，边频带幅值比正常齿轮高出11dB。断齿故障模型移除一个完整轮齿，冲击能量集中在啮合频率的奇数倍频，仿真数据与试验台实测值误差小于5%。
（2）刚柔耦合多体动力学箱体模型：
建立齿轮箱刚柔耦合模型，将箱体视为柔性体（模态中性文件导入），齿轮视为刚体。在不同转速（500-3000rpm）和负载（10-100Nm）下仿真，提取箱体表面测点振动信号。揭示了转速增加造成边频带展宽，负载增加使啮频幅值线性增长但边频带相对幅值下降的规律。生成108种工况仿真数据，填补了小样本条件下齿轮故障数据空白。
（3）融合仿真与真实数据的CBAM-STCN诊断网络：
针对真实样本稀缺，将仿真数据与少量真实数据混合训练。模型采用时间卷积网络（TCN）为主干，嵌入卷积块注意力模块（CBAM）同时关注通道和空间特征。当真实数据每类仅20个时，加入仿真数据（每类200个）后诊断准确率从67%提升至94.5%。在两种未知工况下准确率分别达94.5%和93.7%，且对不同程度裂纹（轻度、中度、重度）的分类准确率均超过90%，优于纯真实数据训练的模型（82%）。
import numpy as np from scipy.signal import find_peaks import torch import torch.nn as nn from torch.nn.utils import weight_norm class Chomp1d(nn.Module): def __init__(self, chomp_size): super().__init__() self.chomp_size = chomp_size def forward(self, x): return x[:, :, :-self.chomp_size].contiguous() class TemporalBlock(nn.Module): def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding, dropout=0.2): super().__init__() self.conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size, stride=stride, padding=padding, dilation=dilation)) self.chomp1 = Chomp1d(padding) self.relu1 = nn.ReLU() self.dropout1 = nn.Dropout(dropout) self.conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size, stride=stride, padding=padding, dilation=dilation)) self.chomp2 = Chomp1d(padding) self.relu2 = nn.ReLU() self.dropout2 = nn.Dropout(dropout) self.net = nn.Sequential(self.conv1, self.chomp1, self.relu1, self.dropout1, self.conv2, self.chomp2, self.relu2, self.dropout2) self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None self.relu = nn.ReLU() def forward(self, x): out = self.net(x) res = x if self.downsample is None else self.downsample(x) return self.relu(out + res) class CBAM_STCN(nn.Module): def __init__(self, num_inputs=1, num_channels=[32,64,128], kernel_size=3, dropout=0.2, num_classes=5): super().__init__() layers = [] num_levels = len(num_channels) for i in range(num_levels): dilation = 2 ** i in_channels = num_inputs if i == 0 else num_channels[i-1] out_channels = num_channels[i] layers += [TemporalBlock(in_channels, out_channels, kernel_size, stride=1, dilation=dilation, padding=dilation*(kernel_size-1), dropout=dropout)] self.tcn = nn.Sequential(*layers) self.cbam = CBAM(num_channels[-1], reduction=8) self.fc = nn.Linear(num_channels[-1], num_classes) def forward(self, x): x = self.tcn(x) x = self.cbam(x) x = x.mean(dim=-1) return self.fc(x) class CBAM(nn.Module): def __init__(self, channels, reduction=8): super().__init__() self.avg_pool = nn.AdaptiveAvgPool1d(1) self.max_pool = nn.AdaptiveMaxPool1d(1) self.fc = nn.Sequential(nn.Linear(channels, channels//reduction, bias=False), nn.ReLU(), nn.Linear(channels//reduction, channels, bias=False)) self.sigmoid = nn.Sigmoid() def forward(self, x): b, c, _ = x.size() avg = self.avg_pool(x).view(b,c) max = self.max_pool(x).view(b,c) avg_out = self.fc(avg) max_out = self.fc(max) att = self.sigmoid(avg_out + max_out).view(b,c,1) return x * att如有问题，可以直接沟通
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