基于振动-温度融合的滚动轴承性能退化适应性识别技术的研究
Zhenghui Li1,2, Lixia Ying1, Liwei Zhan2
1College of Mechanical and Electrical Engineering, Harbin Engineering Univesity, Harbin 150500, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究引入了一种振动-温度融合方法,用于准确评估滚动轴承性能退化. 这种新的方法增强了健康指标的表征和生命阶段的识别,优于仅使用振动的方法.
科学领域:
- 机械工程 机械工程
- 状态监控 状态监控
- 数据科学数据科学数据科学
背景情况:
- 传统的滚动轴承性能降解评估严重依赖于振动信号,往往导致识别过渡状态的准确性较低.
- 整合多源传感器数据,如温度,可能会提高诊断能力.
研究的目的:
- 开发基于振动-温度融合的自适应方法,用于增强滚动轴承性能降解的评估.
- 提高识别轴承生命阶段过渡的准确性.
主要方法:
- 从振动和温度信号构建了一个多维的时间频率特征集.
- 引入了特征选的复合灵敏度指数 (CSI),随后进行了相互信息聚类和对参数重新选择的权优化.
- 使用自关核回归 (AFF-AAKR) 和时间残余分析来增强健康指标 (HI) 描述的适应性特征融合.
- 开发了一种双重标准的自适应式自下而上的合并算法 (DC-ABUM) 用于生命阶段识别.
主要成果:
- 拟议的振动-温度融合方法与传统的只有振动的方法相比,显示出更高的性能.
- 整合温度数据和先进的特征工程改善了轴承退化趋势的表征.
- 适应融合和生命阶段识别算法有效捕获过渡状态.
结论:
- 振动-温度融合为滚动轴承性能退化评估提供了更强大,更准确的方法.
- 开发的自适应方法,包括CSI,AFF-AAKR和DC-ABUM,显著提高了轴承健康和剩余使用寿命的识别.
- 这种融合战略为提高条件监测系统的可靠性提供了一个有希望的方向.
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