数值模型驱动的传动系统故障个性化诊断原理  

Numerical Model Driving Personalized Diagnosis Principle for Fault Detection in Mechanical Transmission Systems

作　　者：向家伟[1] XIANG Jiawei(College of Mechanical&Electrical Engineering,Wenzhou University,Wenzhou 325035)

机构地区：[1]温州大学机电工程学院,温州325035

出　　处：《机械工程学报》2021年第15期116-128,共13页Journal of Mechanical Engineering

基　　金：国家自然科学基金资助项目(U1909217,U1709208)。

摘　　要：如何获得机械传动系统大量反映实际运行状态的故障样本,是制约人工智能诊断模型走向工程应用的瓶颈。基于个体差异的精准诊断需求,提出机械传动系统个性化故障诊断基本原理,通过建立机械传动系统数值模型,进行仿真分析,获得故障样本,解决故障诊断过程中故障特征信息缺乏的短板,从而激活人工智能诊断方法。以轴承、齿轮传动、转子系统等机械传动系统为例,构建完好结构有限元模型,开展模型修正,获得具有一定精度的仿真模型。预定义多类故障并添加至具有一定精度的有限元模型,计算生成故障样本集,作为人工智能诊断模型的训练样本,用于待诊断测试样本分类。任意选取的支持向量机、极限学习机、卷积神经网络等人工智能诊断模型故障分类实验结果表明:所提出机械传动系统故障诊断的个性化诊断原理,具有较强的普适性与可拓展性。How to obtain a large number of fault samples from mechanical transmission systems under the actual running state is a bottleneck for the engineering application using intelligent diagnosis methods.To meet the requirement of precision diagnosis for individual differences,the basic principle of personalized diagnosis for mechanical faults is proposed.Through the construction of numerical simulation model of mechanical transmission systems,simulations are performed to obtain fault samples.The bottleneck problem of lacking fault feature information in the diagnostic procedures will be resolved to activate the artificial intelligent(AI)diagnosis methods.Taking the bearing,gear transmission,rotor-bearing system for examples,the finite element method(FEM)models of intact structures are firstly constructed to obtain simulation model with a certain precision using model updating techniques.Secondly,predefined several faults and further inserted into the high fidelity FEM model to calculate the fault samples,which severed as training samples of AI diagnostic models to classify testing samples(faults to be diagnosed).Finally,the experimental investigations using the arbitrary selection of support vector machine(SVM),extreme learning machine(ELM)and convolutional neural network(CNN)show that the principle of personalized diagnosis for mechanical faults has strong universality and expansibility.

关 键 词：机械传动系统 故障 个性化诊断 人工智能诊断模型 分类 

分 类 号：TH322[机械工程—机械制造及自动化]