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一种用于目标识别和局部增强非静止信号特征的新时间频率切片提取方法
Chaoyong Ma1, Chen Liang1, Zuhua Jiang1
1Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.
ISA transactions
|January 14, 2024
概括
这项研究引入了一种新的时间频率切片提取方法,以改进故障诊断. 该技术通过突出时间频域中的局部特征来增强弱故障信号,从而导致更准确的故障识别.
科学领域:
- 信号处理 信号处理
- 机械工程 机械工程
- 错误诊断 错误诊断 错误诊断 在
背景情况:
- 盲目的解卷对于故障诊断至关重要,但由于单域分析的局限性,它与弱信号作斗争.
- 现有的方法无法同时捕获时间和频率领域的局部信号特征,从而阻碍了弱故障检测.
- 传统的盲目解卷中的统计平均值提取往往忽略了微妙的故障签名.
研究的目的:
- 提出一种新的时频切片提取方法,用于增强弱故障信号检测.
- 通过强调局部信号特征,提高故障诊断的准确性和有效性.
- 在识别微妙故障影响方面克服传统盲目解卷的局限性.
主要方法:
- 使用短时间里叶变换 (STFT) 进行高时间分辨率光谱计算.
- 从STFT生成具有明显时间波形的多个频率切片.
- 应用构造的波谱特征指数来量化和增强故障特征,通过最大相关性曲解解卷变 (MCKD).
主要成果:
- 拟议的方法成功地增强了所选频段集群内的局部特征,减少噪声干扰.
- 获得了具有更明显故障特征的信号组件,改善了对弱故障的检测.
- 通过模拟信号的验证和滚动轴承外部和内部环的成功故障诊断证实了该方法的有效性.
结论:
- 新的时频切片提取方法与常见的解卷技术相比,提供了更高的性能.
- 这种方法提供了更准确和更有效的故障信息识别,特别是在弱信号.
- 该方法显示了在机械系统中推进故障诊断的巨大潜力.
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01:11Sampling Continuous Time Signal
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01:15Discrete Fourier Transform
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