一种基于RVMD和TELM的轮箱新型故障诊断方法,配有复合混沌灰狼优化器
Xuebin Huang1,2, Anfeng Xu3, Hongbing Liu2
1Hainan College of Foreign Studies, Wenchang, 571321, China.
Scientific reports
|July 9, 2025
概括
这项研究引入了强大的变化模式分解 (RVMD) 和双极端学习机器 (TELM),通过复合混沌灰狼优化器 (CCGWO) 来优化变速箱故障诊断. 拟议的RVMD-CCGTELM方法在识别变速箱故障方面取得了卓越的准确性.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 变速箱故障诊断对于工业机械维护至关重要.
- 像变化模式分解 (VMD) 这样的传统方法对噪声和异常值敏感.
- 现有的机器学习模型可能无法实现最佳的特征提取和分类准确性.
研究的目的:
- 为变速箱提出一个强大而准确的故障诊断方法.
- 为了增强噪音环境的信号分解技术.
- 提高机器学习模型在故障检测中的分类性能.
主要方法:
- 强大的变化模式分解 (RVMD) 用于将耐噪声信号分解为内在模式函数 (IMF).
- 双极端学习机器 (TELM) 用于高级特征提取和分类.
- 复合混沌灰狼优化器 (CCGWO) 优化TELM内核参数,创建CCGTELM模型.
主要成果:
- 与VMD-TELM,VMD-DNN,VMD-CNN,VMD-LSTM,EMD-ELM和WT-ANN相比,RVMD-CCGTELM方法的故障诊断准确度更高.
- RVMD有效处理噪音和异常值,性能优于标准VMD.
- CCGTELM显示了功能提取和分类能力的改进.
结论:
- 拟议的RVMD-CCGTELM是一种高度有效和准确的变速箱故障诊断方法.
- 在噪音条件下,RVMD比VMD提供了显著的优势.
- 通过CCGWO优化,可以提高TELM在复杂故障诊断任务中的性能.
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