旋转机器的故障诊断使用最佳盲解卷方法和混合可逆神经网络
Yangde Gao1, Zahoor Ahmad1, Jong-Myon Kim1,2
1Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
一种新方法提高了旋转机械的诊断,通过使用最佳的盲式解卷来消除信号和混合可逆神经网络来预测轴承的健康状况和剩余使用寿命.
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:
- 旋转机械健康监测对于工业维护至关重要.
- 准确的故障诊断和剩余的使用寿命预测是具有挑战性的.
- 振动信号分析是一种常见的状态监测技术.
研究的目的:
- 开发一种新的方法来预测旋转机械的使用寿命.
- 通过先进的信号处理和机器学习,改进旋转机械的故障诊断.
- 引入一个新的健康指数来评估轴承退化.
主要方法:
- 开发了一种最佳的适应性最大二次循环静止性盲解卷 (OACYCBD) 用于振动信号消噪.
- 利用蒙特卡洛概率密度函数 (PDF) 和交叉来优化消极权重.
- 创建了一个基于信号峰值值和算术平均值的新型健康指数.
- 采用混合可逆神经网络 (HINN),将可逆神经网络和LSTM结合起来,用于剩余的使用寿命预测.
主要成果:
- 该OACYCBD方法有效地拒绝振动信号,保存与故障相关的信息.
- 新的健康指数准确地反映了轴承健康的恶化.
- 与SVM,CNN和LSTM相比,HINN模型在预测剩余的使用寿命方面表现出卓越的表现.
- 在工业数据集的剩余使用寿命预测中,实现了0.485的根平均平方误差 (RMSE).
结论:
- 拟议的OACYCBD和HINN方法为旋转机械预测提供了一个强大而准确的方法.
- 这种技术显著提高了预测剩余的使用寿命和故障诊断能力.
- 这些发现对使用旋转机械的行业的预测性维护策略具有实际意义.
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