核心内容摘要
LabVIEW条码追踪系统：一场代码与效率的优雅 dance
算法特点图拉普拉斯正则化替代黎曼几何解决黎曼几何计算复杂度高的痛点保留数据流形结构的同时提升训练效率k近邻图自适应构建自动学习数据局部邻域结构无需预设黎曼度量解决传统方法对度量敏感的问题物理约束与图正则化的融合图拉普拉斯约束保持数据几何结构物理导数约束确保退化趋势合理性双重正则化防止过拟合算法步骤第一阶段数据智能预处理振动信号分割与增强2560点分段消除边界效应滑动窗口平滑提升信噪比多维度特征自动提取时域RMS、峰值、峭度故障早期识别频域多频段能量分布故障定位小波多分辨率能量冲击特征捕捉第二阶段图智能建模k近邻图自适应构建基于数据密度自动确定邻域大小构建对称连接图保留局部结构图拉普拉斯正则化计算L D - A度矩阵-邻接矩阵平滑约束相似样本应有相似输出第三阶段物理约束融合退化趋势物理建模一阶导数非负约束单调退化允许小幅波动符合实际工况自适应权重学习图正则化权重自学习物理约束权重自调节第四阶段智能训练优化稳定训练策略Xavier初始化 Tanh激活防梯度爆炸AdamW优化器 余弦退火快速收敛梯度裁剪max_norm
0早期停止机制150轮耐心值防止过拟合保留最佳模型状态第五阶段RUL精确预测逐步滚动预测从检查点开始逐步预测阈值触发停止机制不确定性量化多步预测置信区间风险概率评估# Import necessary modules import os import scipy.io import scipy.stats import pywt from matplotlib import pyplot as plt import numpy as np import pandas as pd import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F from sklearn.preprocessing import StandardScaler from sklearn.neighbors import kneighbors_graph print(PyTorch Version:, torch.__version__) # Load data files PHM_path PHM PHM_bearing_files [os.path.join(PHM_path, file) for file in os.listdir(PHM_path)] # Enhanced feature extraction def mat_to_arr_enhanced(file): Enhanced feature extraction h scipy.io.loadmat(file)[h].reshape(-
h2 h.reshape(-1,
# Basic features kurtosis np.array([scipy.stats.kurtosis(i) for i in h2]) rms np.array([np.mean(i**
**
5 for i in h2]) rms np.convolve(rms, [
3,
4,
3], modesame) ma np.array([np.max(np.abs(i)) for i in h2]) # Time-frequency features wavelet_features [] for segment in h2: coeffs pywt.wavedec(segment, db4, level
energies [np.sum(c**
for c in coeffs] wavelet_features.append(energies) wavelet_features np.array(wavelet_features) # Frequency domain features freq_features [] for segment in h2: fft_vals np.abs(np.fft.rfft(segment)) freq_features.append([ np.sum(fft_vals[:10]), np.sum(fft_vals[10:50]), np.sum(fft_vals[50:]), np.argmax(fft_vals) ]) freq_features np.array(freq_features) # Combine features all_features np.concatenate([ rms.reshape(-1,
, ma.reshape(-1,
, kurtosis.reshape(-1,
, wavelet_features, freq_features ], axis
FPT int(len(h
) * 1700 / 2560 print(fFault Progression Time (FPT): {FPT:.2f}, Feature Dimension: {all_features.shape}) return h, FPT, all_features # Graph Laplacian Regularized PINN class GraphLaplacianPINN(nn.Module): Graph Laplacian Regularized Physics-Informed Neural Network def __init__(self, input_dim1, hidden_dim32, dropout_rate
0.
: super(GraphLaplacianPINN, self).__init__() self.input_dim input_dim self.hidden_dim hidden_dim # Network architecture self.network nn.Sequential( nn.Linear(input_dim, hidden_dim), nn.Tanh(), nn.Dropout(dropout_rate), nn.Linear(hidden_dim, hidden_dim //
, nn.Tanh(), nn.Dropout(dropout_rate), nn.Linear(hidden_dim // 2,
) # Graph Laplacian regularization parameter self.graph_weight nn.Parameter(torch.tensor(
0.
) # Physics constraint weight self.physics_weight nn.Parameter(torch.tensor(
0.
) # Initialize weights self._initialize_weights() print(fGraph Laplacian PINN: Hidden Layer{hidden_dim}, Dropout{dropout_rate})参考文章基于图拉普拉斯正则化物理信息神经网络的机械退化趋势预测Pytorch - 哥廷根数学学派的文章https://zhuanlan.zhihu.com/p/1999885812414838612工学博士担任《Mechanical System and Signal Processing》审稿专家担任《中国电机工程学报》优秀审稿专家《控制与决策》《系统工程与电子技术》《电力系统保护与控制》《宇航学报》等EI期刊审稿专家。
擅长领域现代信号处理机器学习深度学习数字孪生时间序列分析设备缺陷检测、设备异常检测、设备智能故障诊断与健康管理PHM等。