稀疏度受约束的拉马努贾子空间特征跟踪方法用于复合故障诊断
Jiwang Zhang1, Jian Cheng2, Haiyang Pan2
1China Special Equipment Inspection and Research Institute, Beijing 100000, China.
ISA transactions
|October 11, 2025
概括
一种新的方法,稀疏性受约束的拉马努贾子空间特征跟踪 (SRSFT),通过有效地分离合的故障特征来改善复合故障诊断. 这种技术提高了分解效率和周期检测,用于强大的机械健康监测.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器状态监测 机器状态监测
背景情况:
- 复合故障诊断面临着挑战,因为结合多故障特征和不同强度特征,阻碍了准确的特征提取.
- 现有的方法难以应对旋转机械中多个故障标志的复杂相互作用.
- 对于工业应用来说,需要强大而准确的方法来识别同时发生的故障是非常重要的.
研究的目的:
- 提出一种用于增强复合故障诊断的新方法.
- 在复杂的故障场景中解决功能提取精度不足的局限性.
- 为了提高多断层特征的分解效率和可分离性.
主要方法:
- 开发一种新的稀疏拉马努贾序列,以建立具有固有的周期性的稀疏子空间.
- 为信号映射实施稀疏度受约束的拉马努贾子空间特征跟踪 (SRSFT).
- 整合一个增强的周期估计技术,以改善弱周期检测和稳定性.
主要成果:
- SRSFT 方法有效地将信号映射到一个故障特征更容易分离的领域.
- 在分解效率和复合故障特征提取方面显著提高.
- 与现有方法相比,在多周期脉冲分离和提取方面取得了卓越的性能.
结论:
- 拟议的SRSFT方法为复合故障诊断提供了有效和强大的解决方案.
- 利用周期性和稀疏性,SRSFT擅长分离和提取复合故障特征.
- 该方法在提高机械健康监测系统的可靠性方面显著有前途.
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