Bearing Fault Diagnosis Based on RF-PCA-LSTM Model

Hanting Zhou, Longsheng Cheng, Lehua Teng, Huiming Sun
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引用次数: 1

Abstract

To realize accurate fault diagnosis of rolling bearing under random noise, a novel fault diagnosis method based on random forest (RF) - principal component analysis (PCA) and long short-term memory (LSTM) neural network is proposed in this paper. The vibration signal is decomposed into several intrinsic mode functions (IMFs) with ensemble empirical mode decomposition (EEMD) to eliminate the random noise interference from the original vibration signal. It is vital to choose sensitive features from both the time and frequency domain of IMF components with importance rank by using RF. Then PCA is conducted to eliminate the correlation among sensitive features. On this basis, this paper utilizes LSTM neural network to get better diagnosis performance in complicated working conditions and hybrid faults. Comparing with the traditional feature extraction method, RF-PCA can get fewer but more representative characteristics. At the same time, the introduction of the LSTM neural network can provide a simple and practical resolution for rolling bearing fault diagnosis.
基于RF-PCA-LSTM模型的轴承故障诊断
为了实现随机噪声下滚动轴承的准确故障诊断,提出了一种基于随机森林-主成分分析(PCA)和长短期记忆神经网络的故障诊断方法。利用集成经验模态分解(EEMD)将振动信号分解为多个本征模态函数(IMFs),消除了原始振动信号中的随机噪声干扰。利用射频技术从具有重要等级的IMF分量的时域和频域中选择敏感特征是至关重要的。然后进行主成分分析,消除敏感特征之间的相关性。在此基础上,本文利用LSTM神经网络在复杂工况和混合型故障下获得了较好的诊断性能。与传统的特征提取方法相比,RF-PCA可以获得更少但更具代表性的特征。同时,LSTM神经网络的引入可以为滚动轴承故障诊断提供一种简单实用的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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