局部度优化-自适应性解调重新分配转换用于对非静止机械信号的高级分析
Yuli Niu1, Zhongchao Liang1, Hengshan Wu2
1Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
一种新方法,即局部度优化-适应性调节重新分配转换 (LEOADRT) 增强了复杂机械振动的分析. 这种先进的技术提高了时间频率分辨率,以更好地诊断故障和监控状态.
科学领域:
- 信号处理 信号处理
- 机械工程 机械工程
- 振动分析 振动分析
背景情况:
- 分析复杂的,非静止的机械振动信号是具有挑战性的,因为有多个瞬间频率和紧密的频率山脊.
- 现有的时间频率分析方法经常与显示不相称性和交叉频率干扰的信号作斗争.
研究的目的:
- 引入一种新的时间频率分析方法,即局部度优化 - 适应性调节重新分配转换 (LEOADRT).
- 为了增强复杂的非静止机械振动信号的分析,改进时间频率分辨率.
主要方法:
- LEOADRT 采用了解调术语来将信号组件转换为静止信号,并使用 Rényi 的局部最佳理论优化参数.
- 能源再分配是使用最大局部能源标准来进行的,以精细化时间频率地图山脊.
- 该方法是为具有多个瞬间频率和紧密间隔的频率间隔的信号而设计的.
主要成果:
- 与SBCT,EMCT,VSLCT和GLCT等现有方法相比,LEOADRT表现出卓越的性能.
- 该算法有效地处理复杂的非静止信号,包括那些具有非比例性和频率相距较近的信号.
- 在时间频率分辨率方面取得了显著的改进.
结论:
- LEOADRT提供了一种强大的工具,用于实时分析多组件和交叉频率的机械振动信号.
- 该方法为机械故障诊断,状态监测和预测性维护提供了强有力的支持.
- 这一进步对于需要高分辨率信号分析的复杂工业应用特别有价值.
相关概念视频
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