一种基于NEITD-ADTL-JS算法的新型滚动轴承故障诊断方法
Shi Zhuo1, Xiaofeng Bai2, Junlong Han3
1Aero Engine Corporation of China Harbin Bearing Company, Ltd., Harbin 150500, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究引入了一种新的轴承故障诊断方法,使用噪声消除内在时间尺度分解 (NEITD) 和适应性深度转移学习 (ADTL) 与水母搜索 (JS). 这种方法显著提高了旋转机械的诊断准确性和稳定性.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 轴承故障在旋转机械中至关重要,导致设备故障.
- 传统的故障诊断方法在准确性和稳定性方面扎,特别是在复杂的信号方面.
- 转移学习提供了潜力,但需要有效的信号预处理和参数优化.
研究的目的:
- 开发一种创新的轴承故障诊断方法,以提高转移学习的准确性.
- 引入一种新的信号预处理技术,以提高信号质量.
- 用先进的算法优化故障诊断参数,以获得卓越的性能.
主要方法:
- 一个消除噪音的内在时间尺度分解 (NEITD) 算法用于自适应信号分解和特征提取.
- 一个改进的自适应深度转移学习 (ADTL) 网络,用于增强诊断能力.
- 整合水母搜索 (JS) 算法,以进行故障诊断参数的自适应优化.
主要成果:
- 与传统方法相比,NEITD算法展示了优越的信号分解性能.
- 结合NEITD-ADTL-JS方法在故障诊断准确度上取得了5.29%的改善.
- 拟议的方法显示在各种故障类型中增强了灵敏度和识别能力.
结论:
- 创新的NEITD-ADTL-JS方法显著提高了轴承故障诊断的准确性和稳定性.
- 适应性信号分解和深度学习优化对于有效的故障诊断至关重要.
- 这种方法为旋转机械的可靠状态监测提供了一个有希望的解决方案.
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