基于混合深度自编码网络的轴承故障新颖性检测与故障诊断方法,Electronics

当前位置： X-MOL 学术 › Electronics › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

基于混合深度自编码网络的轴承故障新颖性检测与故障诊断方法
Electronics ( IF 2.6 ) Pub Date : 2023-06-26 , DOI: 10.3390/electronics12132826
Yuanyuan Zhao ₁ , Huijuan Hao ₁ , Yu Chen ₁ , Yu Zhang ₁

Affiliation

当机械设备发生故障时，故障可能不属于任何已知类别，现有的深度学习方法常常将此类故障错误地归入已知类别，从而导致错误的故障诊断。为了解决机械设备故障诊断中新型故障识别的挑战，提出一种基于混合深度自编码网络的轴承故障新颖性检测与故障诊断方法。首先，构建了一个输入和两个输出的混合深度自动编码器网络。然后将原始数据输入网络以获得其低维表示和重建数据。通过根据重构误差设置阈值，可以检测新类别故障，同时可以根据低维特征对已知故障进行分类。实验结果表明，该方法在 CWRU 轴承数据集上的新型类别识别（已知故障分类）识别准确率达到 98.59%（100%），在 Paderborn 数据集上达到 96.79%（98.53%），在 Paderborn 数据集上达到 84.34%（97.03%）。）在 MFPT 数据集上。因此，混合深度自编码网络不仅能够准确检测未知类型的故障，而且能够有效地对已知故障类型进行分类，表现出优异的故障识别和分类能力。

"点击查看英文标题和摘要"

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network

In the event of mechanical equipment failure, the fault may not belong to any known category, and existing deep learning methods often misclassify such faults into a known class, leading to erroneous fault diagnosis. In order to address the challenge of identifying new types of faults in mechanical equipment fault diagnosis, this paper proposes a novelty detection and fault diagnosis method for bearing faults based on a hybrid deep autoencoder network. Firstly, a hybrid deep autoencoder network with one input and two outputs was constructed. The original data were then fed into the network to obtain its low-dimensional representation and reconstructed data. By setting a threshold based on the reconstruction error, novel class faults can be detected, while known faults can be classified based on low-dimensional features. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 98.59% (100%) for novel class identification (known fault classification) on the CWRU bearing dataset, 96.79% (98.53%) on the Paderborn dataset, and 84.34% (97.03%) on the MFPT dataset. Therefore, the hybrid deep autoencoder network not only accurately detects unknown types of faults but also effectively classifies known fault types, demonstrating excellent fault identification and classification capabilities.

更新日期：2023-06-27

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南