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基于多约束非负矩阵因数分解的复合故障信号的分离和提取
Mengyang Wang1,2, Wenbao Zhang1, Mingzhen Shao1
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
本研究引入了一种多约束非负矩阵因子化 (NMF) 方法,用于分离信号和诊断旋转机械的故障. 增强的NMF有效地分离多源信号,并识别轴承故障特征.
科学领域:
- 信号处理 信号处理
- 机械工程 机械工程
- 机器学习 机器学习
背景情况:
- 未确定的盲源分离对传统的非负矩阵因子化 (NMF) 是一个挑战.
- 现有的NMF方法难以有效地增强本地特征并减少冗余组件.
研究的目的:
- 提出一种使用多约束非负矩阵因子化 (NMF) 的新信号分离方法.
- 通过分离多源信号来提高旋转机械故障诊断的准确性.
主要方法:
- 应用的短时间里叶变换 (STFT) 具有Sine-bell窗口用于时间频率分析.
- 在NMF中引入β-分歧和决定因素约束,以增强特征信息并减少冗余.
- 使用参数WK进行过和封面频谱分析以检测故障特征.
主要成果:
- 成功地从单一通道中分离了多源振动信号.
- 通过模拟和实验证明了轴承故障诊断的有效性.
- 拟议的多约束NMF在复合故障诊断方面表现优于传统的NMF.
结论:
- 多约束NMF方法对于旋转机械的信号分离和故障诊断是有效的.
- 与传统的NMF相比,这种方法提供了更高的性能,特别是在复杂的故障场景中.
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