基于随机LSTM块映射特征提取的旋转机械故障诊断方法  

Fault diagnosis method of rotating machinery based on randomized LSTM block mapping feature extraction

作　　者：杨金龙 董绍江[1] 牟小燕 YANG Jin-long;DONG Shao-jiang;MOU Xiao-yan(School of Mechantronics and Vehicle Engineering,Chongqing Jiaotong University,Chongqing 400074,China;School of Mechanical Engineering,Chongqing Industry Polytechnic College,Chongqing 401120,China)

机构地区：[1]重庆交通大学机电与车辆工程学院,重庆400074 [2]重庆工业职业技术学院机械工程学院,重庆401120

出　　处：《陕西科技大学学报》2024年第4期142-153,共12页Journal of Shaanxi University of Science & Technology

基　　金：重庆市科技创新领军人才支持计划项目(CSTCCCXLJRC201920);重庆市教委科学技术研究项目(KJZD-K202300711,KJQN202203207);重庆市高校创新研究群体项目(CXQT20019)。

摘　　要：针对旋转机械失效机理复杂,特征信息差异大,导致的传统诊断模型依赖先验知识,准确率低,适应性差的难题.提出一种基于随机量化数据增强-随机LSTM(Long Short Term Memory)块映射特征提取-随机配置网络(Randomized Quantization-Randomized LSTM Block Mapping Method-Stochastic Configuration Network,简称RQ-RLBM-SCN)的旋转机械故障诊断方法.首先,为了解决失效机械特征信息小子样,训练样本不足的难题,使用随机量化数据增强将多传感器原始数据样本进行扩充,从而提高模型的适应性、准确率和缓解过拟合问题.其次用随机LSTM块映射方法来提取特征,解决SCN不擅长提取时序数据特征难的问题;然后使用随机配置网络(SCN)进行分类,SCN可以动态配置参数,无需反向传播来更新参数,在保证学习率的同时,还有效的避免梯度爆炸或梯度消失等问题.采用RQ-RLBM-SCN方法能准确识别出轴承和齿轮故障,在10次重复实验中,轴承和齿轮的多传感器数据集上的平均准确率分别达到99.80%、98.75%均高于原始SCN、TSC-SCN、VMD-SCN、SVM和KNN故障诊断方法;该方法可以为建立旋转机械的健康监测模型提供动态方法和诊断思路.In light of the complexity of failure mechanisms in rotating machinery and the significant differences in feature information,traditional diagnostic models often rely on prior knowledge,leading to low accuracy and poor adaptability.We propose a rotating machinery fault diagnosis method named"Randomized Quantization-Randomized LSTM Block Mapping Method-Stochastic Configuration Network"(RQ-RLBM-SCN).To address the challenges posed by limited feature information in failing machinery and insufficient training samples,we employ random quantization data augmentation to expand the original data samples from multiple sensors.This enhances the model′s adaptability,accuracy and mitigating overfitting.Next,we employ a random LSTM block mapping method to extract features,solving the difficulty of extracting temporal data features in stochastic configuration network(SCN).Subsequently,we utilize a stochastic configuration network(SCN)for classification.SCN dynamically configures parameters without the need for backpropagation to update parameters,ensuring a proper learning rate while effectively avoiding issues such as gradient explosion or vanishing.Research results indicate that the RQ-RLBM-SCN method can accurately identify bearing and gear faults.In 10 repeated experiments,the average accuracy on the multi-sensor dataset for bearings and gears reaches 99.80%and 98.75%respectively which are higher than the original SCN,TSC-SCN,VMD-SCN,SVM and KNN fault diagnosis methods.This approach provides a dynamic method and diagnostic insight for establishing a health monitoring model for rotating machinery.

关 键 词：随机配置网络 故障诊断 旋转机械 多传感器 长短时记忆网络 

分 类 号：TH17[机械工程—机械制造及自动化] TP183[自动化与计算机技术—控制理论与控制工程]