非线性机械响应分析和卷积神经网络使得单跨转子轴承系统的诊断成为可能
Bing Qian1, Yinhui Cai1, Yinkang Ran1
1CHN Energy Dadu River Repair & Installation Co., Ltd., Leshan, 614900, China.
Scientific reports
|May 6, 2024
概括
本研究建立了一个旋转轴承测试台,用于诊断旋转机械故障. 一个卷积神经网络准确地识别了螺旋曲和裂纹断层,实现了100%的分类准确性.
科学领域:
- 机械工程 机械工程
- 机器学习 机器学习
背景情况:
- 旋转机械在电力和航空领域至关重要,但在运行过程中容易发生故障.
- 有效的故障诊断对于机器的维护和运营效益至关重要.
研究的目的:
- 实验分析和诊断一个单跨转子轴承系统中的三个不同的螺旋故障.
- 开发和验证一个卷积神经网络 (CNN) 准确的故障分类.
主要方法:
- 建立了一个单跨旋转轴承的测试台.
- 实验分析了螺杆曲和螺杆裂纹故障的机械反应 (轴轨迹和光谱) (带有和没有末端负载).
- 应用卷积神经网络用于基于实验数据的故障诊断.
主要成果:
- 对于每个断层类型,观察到不同的机械反应,包括轴轨迹和光谱.
- 与轴心曲缺陷 (1×,2×,3×) 相比,轴心裂纹缺陷激发了额外的频率组件 (4×,5×).
- 在这三个故障条件下,CNN在训练和测试数据集上实现了100%的分类准确性.
结论:
- 这项研究证明了分析机械反应用于旋转机械故障诊断的有效性.
- 卷积神经网络可以根据它们独特的信号特征准确地区分轴曲和裂纹断层.
- 开发的CNN模型显示了在旋转机械中实时,可靠的故障分类的巨大潜力.
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