基于自适应性最大二阶循环静止盲解卷和卷积式LSTM自编码器的旋转机器剩余使用寿命的预测
Yangde Gao1, Zahoor Ahmad1, Jong-Myon Kim1
1Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.
Sensors (Basel, Switzerland)
|April 27, 2024
概括
本研究引入了一种先进的方法,用于预测旋转机械的剩余使用寿命 (RUL). 该方法通过有效分析机械健康状况和预测故障,提高了可靠性.
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:
- 预测旋转机械的剩余使用寿命 (RUL) 对运行可靠性和最大限度地减少经济损失至关重要.
- 现有的方法往往在噪声干扰和对振动信号的健康状况准确分析方面扎.
研究的目的:
- 提出一种有效的方法,用于在旋转机械中预测RUL.
- 开发一种强大的方法,用于特征提取,健康指数分析和使用振动信号进行RUL预测.
主要方法:
- 采用了适应性最大二次循环稳定性盲解卷 (ACYCBD) 来对振动信号进行噪声过.
- 设计了一种基于峰值属性的新型健康指数 (HI),用于对轴承退化的敏感分析.
- 开发了一个卷积LSTM自编码器 (ALSTM) 模型用于健康趋势预测.
主要成果:
- 该ACYCBD有效过噪音,使得健康指数的准确分析.
- 新型HI显示出对轴承降解的高度敏感性.
- 在RUL预测准确度方面,ALSTM模型的表现优于SVM,CNN,LSTM,GRU和DTGRU.
结论:
- 拟议的ACYCBD和ALSTM方法为旋转机械中的RUL预测提供了卓越的性能.
- 这种方法提高了预后和健康管理系统.
- 该研究强调了集成信号处理和深度学习在机器诊断方面的潜力.
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