滚动轴承的故障诊断基于HPSO算法优化CNN-LSTM神经网络的神经网络
He Tian1,2, Huaicong Fan1,2, Mingwen Feng1,2
1National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China.
本研究介绍了一种优化的CNN-LSTM模型,使用混合粒子群优化 (HPSO) 进行精确的滚动轴承故障诊断. 这种新的方法显著提高了诊断准确度,防止设备故障.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 状态监控 状态监控
背景情况:
- 滚动轴承的质量对于轴的性能和旋转精度至关重要.
- 轴承的早期故障诊断对于防止运行损失和确保机器可靠性至关重要.
- 传统方法通常依赖于经验参数设置,限制动态优化.
研究的目的:
- 为了提高轴承故障诊断的准确性.
- 提出一种新的CNN-LSTM轴承故障诊断模型,通过混合粒子群优化 (HPSO) 进行优化.
- 克服现有模型中静态参数设置的局限性.
主要方法:
- 开发一个卷积神经网络-长期短期记忆 (CNN-LSTM) 模型.
- 应用混合粒子群集优化 (HPSO) 算法来动态优化CNN-LSTM参数.
- 验证HPSO-CNN-LSTM模型在轴承故障分类中的性能.
主要成果:
- 由HPSO优化的CNN-LSTM模型实现了99.2%的故障诊断分类准确率.
- 与传统的CNN (6.6%),LSTM (9.2%) 和标准的CNN-LSTM (5%) 模型相比,观察到显著的准确性改善.
- HPSO算法在优化轴承故障诊断模型参数方面表现出卓越的性能.
结论:
- 该HPSO算法有效优化CNN-LSTM参数,克服经验限制.
- HPSO-CNN-LSTM模型为轴承故障诊断提供了一种可行且非常准确的解决方案.
- 这种方法显著提升了滚动轴承的状态监测和预测性维护策略.
更多相关视频
07:15Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
06:45Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
Published on: October 28, 2022
相关概念视频
Bearings: Problem Solving
Rolling Resistance: Problem Solving
Distributed Loads: Problem Solving
Residual Stresses in Circular Shafts
Journal Bearings
To better understand the concept of journal bearings, consider a rope winch with dry or...
Rolling Resistance
For instance, imagine a hard cylinder rolling on a comparatively soft surface. The cylinder's weight compresses the surface beneath it. As the cylinder moves, the material in front of it slows down...
