基于优化算法融合卷积神经网络的滚动轴承的智能故障诊断算法
Qiushi Wang1,2, Zhicheng Sun1,2, Yueming Zhu1,2
1Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
这项研究引入了用于滚动轴承的先进故障诊断方法,使用具有注意力机制和优化超参数的1D-CNN. 这种新的方法提高了机械设备的诊断准确度和特征表示.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 滚动轴承的故障威胁到机械设备的运行,并造成财务损失.
- 传统的卷积神经网络 (CNN) 在特征表示和错误诊断的超参数确定方面存在局限性.
研究的目的:
- 为滚动轴承提出一个改进的故障诊断方法.
- 为了提高诊断准确度和特征表示能力的CNNs.
- 为了高效地优化CNN的超参数.
主要方法:
- 一个注意力机制与1D-CNN集成,以改善故障特征表示.
- 一个结合差异进化 (DE) 和灰狼优化 (GWO) 的群集智能算法被开发用于超参数优化.
- 应用DE-GWO-CNN模型用于滚动轴承故障诊断,使用CWRU和JNU数据集.
主要成果:
- 与传统模型相比,提出的DE-GWO-CNN方法显示了诊断准确度的提高.
- 增强型号显示出优越的抗噪声能力.
- 优化算法提高了对汇率的准确性和搜索效率.
结论:
- DE-GWO-CNN算法是用于滚动轴承故障诊断的可靠和有效方法.
- 该方法准确地识别和分类故障,有助于及时维护.
- 这种方法有助于最大限度地减少设备故障和操作不稳定.
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