滚动轴承故障诊断的振动信号分析基于深浅的融合特征
Ahmed Chennana1, Ahmed Chaouki Megherbi2, Noureddine Bessous3
1Dept. Electrical Engineering, Laboratory of LI3C, University of Mohamed Khider, Biskra, Algeria. ahmed.chennana@univ-biskra.dz.
Scientific reports
|March 19, 2025
概括
这项研究引入了一种先进的轴承故障诊断系统,将新的手工制作功能 (MBH-LPQ) 与深度学习 (VGGish) 结合起来,以提高机器的可靠性. 混合模型在杂的数据集上实现了非常高的准确性,高达100%,防止了工业停机.
科学领域:
- 工程 工程师 工程师 工程师
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 轴承故障诊断对于工业机械的可靠性和防止停机时间至关重要.
- 传统方法与不同的操作条件和不断变化的工业需求作斗争.
- 需要改进,强大的轴承故障诊断系统.
研究的目的:
- 提出一个高效和强大的系统来诊断轴承故障,使用深层和浅层特征的组合.
- 为了提高诊断性能,利用手工制作和深度学习功能的互补优势.
- 解决复杂工业环境中现有的故障检测方法的局限性.
主要方法:
- 开发了一种新的手工制作的浅特征描述器:局部相量化的多块组图 (MBH-LPQ).
- 使用音频谱图,利用从经过音频训练的VGGish卷积神经网络 (CNN) 模型中的深度特征.
- 在使用加权总和 (WS) 的得分水平上,结合浅 (MBH-LPQ) 和深 (VGGish) 的特征.
主要成果:
- 拟议的混合型号在轴承故障诊断方面实现了高分类准确性.
- MBH-LPQ 浅层描述器表现出色的性能.
- 经过音频训练的VGGish模型在任务中表现出最佳表现,在CWRU数据集上达到98.95%,在PU数据集上达到100%.
- 综合方法显著提高了噪音数据集的诊断准确性.
结论:
- 混合式方法有效地结合了浅层和深层特征,用于优越的轴承故障诊断.
- 拟议的系统提供了一个强大的解决方案,用于在具有挑战性的,杂的工业条件下识别轴承故障.
- 这项研究有助于加强机械维护,减少停机时间和提高工业生产率.
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