摘 要
机械电子系统在现代工业中发挥着至关重要的作用，其故障诊断对于保障系统的稳定运行和提高生产效率具有重要意义。传统故障诊断方法存在对复杂故障特征提取能力不足、诊断精度不高以及适应性差等问题。本研究基于深度学习技术，旨在构建一种高效准确的机械电子系统故障诊断模型。通过引入卷积神经网络（CNN）与长短期记忆网络（LSTM）相结合的混合架构，充分利用CNN强大的局部特征提取能力和LSTM对时序数据的记忆特性，实现对机械电子系统多源异构数据的有效处理。实验采用某型工业机器人关节部位为研究对象，采集正常及多种典型故障状态下的振动信号、电流信号等作为样本数据集。经过对比分析表明，所提方法不仅能够自动挖掘深层次故障特征，而且在不同工况条件下均展现出较高的诊断准确率，平均准确率达到96.7%，相较于传统方法有显著提升。
关键词：机械电子系统故障诊断 深度学习 卷积神经网络
Abstract
Mechanical electronics system plays a vital role in modern industry, and its fault diagnosis is of great significance to ensuring the stable operation of the system and improving the production efficiency. Traditional fault diagnosis methods have some problems, such as insufficient ability to extract complex fault features, low diagnostic accuracy and poor adaptability. This study is based on deep learning techniques to construct an efficient and accurate fault diagnosis model for MES. By introducing the hybrid architecture combining convolutional neural network (CNN) and long and short-term memory network (LSTM), the powerful local extraction ability of CNN feature extraction and LSTM memory characteristics of temporal data can realize the effective processing of multi-source heterogeneous data of mechanical and electronic system. In the experiment, the joint site of a certain industrial robot was used as the research ob ject, and the vibration signals and current signals under normal and various typical fault states were collected as the sample data set. After comparative analysis, it shows that the proposed method can not only automatically mine the deep fault features, but also show high diagnostic accuracy under different working conditions, with an average accuracy of 96.7%, which is significantly improved compared with the traditional method.
Keyword:Fault Diagnosis Of Mechatronic Systems Deep Learning Convolutional Neural Network
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3本文研究方法 2
2深度学习理论基础 2
2.1深度学习基本原理 2
2.2常用深度学习算法 3
2.3深度学习在故障诊断中的应用 3
3机械电子系统故障特征分析 4
3.1故障类型与成因 4
3.2数据采集与预处理 5
3.3特征提取与选择 5
4基于深度学习的故障诊断模型构建 6
4.1模型架构设计 6
4.2模型训练与优化 7
4.3模型评估与验证 7
结论 8
参考文献 9
致谢 10
