基于ITD-ILBP-汉克尔矩阵的滚动轴承故障识别
Mingyue Yu1, Ziru Ma1, Yingdong Gao1
1School of Automation, Shenyang Aerospace University, Shenyang, Liaoning Province 110136, China.
ISA transactions
|September 6, 2025
概括
这项研究引入了使用内在时间尺度分解 (ITD) 和新一代1D局部二进制模式 (1D-LBP) 量子化的改进的轴承故障诊断方法. 通过减少噪声和提取关键故障特征,ITD-ILBP-Hankel方法提高了准确性.
科学领域:
- 机械工程
- 信号处理
- 错误诊断
背景情况:
- 轴承故障表现出非静态性和非线性,使故障特征的提取复杂化.
- 传统的1D局部二进制模式 (1D-LBP) 对噪声敏感,导致不准确的量化和诊断.
- 现有的方法难以有效地隔离和分析复杂的振动信号中的故障特征.
研究的目的:
- 通过解决噪声干扰和不精确的量化来提高轴承故障诊断的准确性.
- 提出一种新的联合方法,将改进的1D-LBP与内在时间尺度分解 (ITD) 和汉克尔矩阵分析结合起来.
- 开发一个可靠的信号处理策略,以可靠地识别轴承故障特征.
主要方法:
- 一个信号预处理策略,涉及到第一阶差异和ITD分解,以获得适当的旋转元件 (PRC).
- 一个改进的1D-LBP量化标准,使用平方根平均值 (RMS) 作为减轻噪声和极端值影响的门.
- 在低维空间中构建汉克尔矩阵并重建信号以减少干扰并提取隐藏的特征.
主要成果:
- 拟议的ITD-ILBP-汉克尔方法有效地降低了噪声,并提高了1D-LBP定量化的准确性.
- 组件智能信号处理和重建成功突出轴承故障特征.
- 错误的特征频率被准确地提取,使得可靠的错误类型判断.
结论:
- 与经典技术相比,开发的ITD-ILBP-Hankel方法在轴承故障诊断方面有了显著的改进.
- 新的量子化标准和信号处理策略提高了故障特征提取的稳定性和有效性.
- 这种方法为在复杂的工业环境中诊断轴承故障提供了可靠的工具.
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