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A new ensemble convolutional neural network with diversity regularization for fault diagnosis
Journal of Manufacturing Systems ( IF 12.1 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.jmsy.2020.12.002
Long Wen , Xiaotong Xie , Xinyu Li , Liang Gao

Abstract Fault diagnosis is an essential technique to ensure the safety in modern industry. With the development of smart manufacturing, deep learning (DL) has been widely used to handle with massive mechanical data in fault diagnosis. However, the individual DL method suffers from the low generalization ability. In this research, a new improved snapshot ensemble Convolutional Neural Network (ISECNN) is proposed in order to obtain a stable and well-performed DL based fault diagnosis method. ISECNN applies the diversity regularization to generate several local minima and keeps their diversity during the training process, as the increasing of the diverse would promote the generalization ability of the group of local minima. Then, ISECNN combines all the local minima to form the ensemble method. The proposed ISECNN has been conducted on two famous bearing datasets. The prediction accuracy and the standard deviation are applied as the criterion. The experimental results show that ISECNN can increase the generalization ability without decreasing the prediction accuracy. ISECNN is also compared with traditional DL and machine learning methods, and the results validate the potential performance of ISECNN in the fault diagnosis field.

中文翻译:

一种用于故障诊断的具有多样性正则化的新集成卷积神经网络

摘要 故障诊断是保障现代工业安全的一项重要技术。随着智能制造的发展,深度学习(DL)在故障诊断中被广泛用于处理海量机械数据。然而,单个 DL 方法的泛化能力较低。在这项研究中,提出了一种新的改进快照集成卷积神经网络(ISECNN),以获得稳定且性能良好的基于​​深度学习的故障诊断方法。ISECNN 应用多样性正则化来生成多个局部最小值并在训练过程中保持它们的多样性,因为多样性的增加会促进局部最小值组的泛化能力。然后,ISECNN 将所有局部最小值组合起来形成集成方法。提议的 ISECNN 已在两个著名的轴承数据集上进行。应用预测精度和标准偏差作为标准。实验结果表明,ISECNN可以在不降低预测精度的情况下提高泛化能力。ISECNN 还与传统的深度学习和机器学习方法进行了比较,结果验证了 ISECNN 在故障诊断领域的潜在性能。
更新日期:2020-12-01
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