强大的分数低顺序自适应线性切尔普莱特变换及其应用于故障分析的应用
Junbo Long1, Changshou Deng1, Haibin Wang2
1College of Electronic Information Engineering, Jiujiang University, No. 551, Qianjin East Road, Jiujiang 332000, China.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
新的小数下级方法 (FLOACT和FLOASCT) 增强了用于机械故障检测的时间频率分析. 这些强大的技术在杂的环境中提高了信号清晰度,有助于准确识别故障.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 振动分析 振动分析
背景情况:
- 传统的时间频率分析 (TFA) 由于复杂的噪声而与非高斯式机械故障信号扎.
- 无限方差过程噪声和高斯色噪声会降低时间频率表示 (TFR).
研究的目的:
- 提出可靠的分数下级自适应线性片变换 (FLOACT) 和分数下级自适应缩放片变换 (FLOASCT) 方法.
- 为了抑制混合复杂噪声,以改善机械故障振动信号的TFA.
主要方法:
- 开发和详细说明FLOACT和FLOASCT算法的计算步骤.
- 利用小数低阶统计的噪声不敏感性质.
主要成果:
- 改进的FLOACT和FLOASCT方法有效地分析具有短频间隔和交叉频率轨迹的多组件信号.
- 即使在强冲动背景噪声环境中也表现出良好的性能.
结论:
- 拟议的FLOACT和FLOASCT方法在复杂的环境中显示出机械外部赛道故障分析的稳定性和适应性.
- 在较低的信号噪声比率 (MSNR) 中实现了良好的估计准确性和有效性.
相关概念视频
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