双域冲动复杂度指数引导投影代方法基于优化器-特征模式分解 (DICI引导PIMO-FMD):在强噪声条件下进行轴承故障诊断的强有力的方法
Dongning Chen1, Qinggui Xian1, Chengyu Yao2
1School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项研究引入了一个新的双域冲动复杂性指数 (DICI),以改善在杂的工业环境中进行轴承故障检测. 该方法可自适应地优化特征模式分解 (FMD) 的参数,提高故障冲击检测,即使信号噪声比低.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 状态监控 状态监控
背景情况:
- 轴承是易受噪声干扰的关键工业部件,在振动分析中降低信号与噪声比 (SNR).
- 传统方法在低SNR条件下难以有效地提取轴承故障信息.
- 功能模式分解 (FMD) 是有希望的,但受到参数灵敏度和手动设置的限制.
研究的目的:
- 开发特征模式分解 (FMD) 的自适应参数优化方法,以改善轴承故障检测.
- 为了增强从低SNR的振动信号中提取故障冲击组件.
- 在杂的工业环境中提高轴承故障诊断的稳定性和可靠性.
主要方法:
- 提出了一种双域冲动复杂性指数 (DICI),将时间域和频域特征结合起来,用于病参数评估.
- 采用基于投影-代方法的优化器 (PIMO) 来进行口病参数的自适应优化.
- 使用故障频率相关性 (FFC) 标准进行敏感组件选择和封面光谱分析以识别故障模式.
主要成果:
- 拟议的DICI-PIMO方法在低SNR条件下的现有方法相比,在识别轴承故障方面表现优越.
- 适应性参数优化显著提高了口病的稳定性和有效性.
- 通过模拟和真实工业振动信号成功验证.
结论:
- DICI-PIMO方法为在具有挑战性,高噪音的工业环境中进行轴承故障诊断提供了强大而适应性的解决方案.
- 这种方法提高了振动信号分析的能力,以有效监测状态.
- 这些发现表明,在低SNR的情况下检测轴承故障方面取得了重大进展.
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