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在基于WDCNN-BiLSTM语网络的小样本条件下的滚动轴承故障诊断

Chenxu Bian^1,2, Chunni Jia^3,4, Jibo Li^1,2

¹School of Materials Science and Engineering, University of Science and Technology of China, Shenyang, 110016, China.

Scientific reports

|August 12, 2025

概括

一个新的罗神经网络 (SNN) 使用有限的数据有效地诊断滚动轴承故障. 这种WDCNN-BiLSTM集成模型在特征提取和相似性学习方面表现出色,甚至在噪音的情况下也优于传统方法.

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:

滚动轴承对于旋转机械至关重要,但易受损坏,需要有效的故障诊断.
传统的深度学习模型面临着有限的故障样本的挑战,导致过度拟合和糟糕的泛化.

研究的目的:

开发一个强大的滚动轴承故障诊断模型,特别是解决有限的培训数据的挑战.
通过度量学习增强模型识别特征空间样本相似性的能力.

主要方法:

一个新的罗神经网络 (SNN) 模型,集成深层卷积神经网络与宽第一层内核 (WDCNN) 和双向长短期记忆 (BiLSTM).
一个特征提取系统,它结合了WDCNN用于局部空间特征和BiLSTM用于全球时间依赖.
在小样本条件下,在SNN框架内进行度量学习,以构建在小样本条件下的歧视性特征空间.

主要成果:

拟议的SNN模型仅使用90个训练样本,实现了83.47% (CWRU数据集) 和61.48% (HUST数据集) 的诊断准确率.
该模型的性能明显优于标准CNN,BiLSTM和CNN-BiLSTM组合模型.
证明了对抗严重噪音干扰的强度.

结论:

拟议的基于WDCNN-BiLSTM的SNN是使用有限数据进行滚动轴承故障诊断的可行和有效工具.
该模型能够从小型样本集中学习,其抗噪能力为现实世界机械监控提供了实际优势.

关键词:

深度学习是一种深度学习.错误诊断是一个错误的诊断.滚动轴承是一个滚动轴承.西安神经网络的神经网络一个小样本小样本.

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