Ensemble deep learning-based fault diagnosis of rotor bearing systems
Ensemble deep learning-based fault diagnosis of rotor bearing systems
复制标题
基于集成深度学习的转子轴承系统故障诊断
DOI：
10.1016/j.compind.2018.12.012
复制
发表时间：
2019-02-01
影响因子：
10
通讯作者：
Chu, Fulei
中科院分区：
文献类型：
--
作者：
Ma, Sai;Chu, Fulei
对于旋转机械而言，早期准确地诊断转子和轴承部件故障具有重要意义。经典的故障诊断模型包括两个关键模块：特征提取和故障分类。为了增强实用性，深度学习模型将这两个模块结合起来，实现了端到端的故障诊断，避免了人工设计特征适应性不足带来的问题。但考虑到故障诊断技术应用场景的广泛性，单一深度模型的应用范围可能存在相应的局限性。为此，本文提出了一种基于多目标优化的集成深度学习诊断方法。该方法以多目标优化算法为集成策略，将卷积残差网络（CRN）、深度置信网络（DBN）和深度自动编码器（DAE）加权集成，实现旋转机械转子和轴承故障的有效诊断。实验结果表明，与其他单一和集成深度模型相比，该方法具有更好的适应性。(C)2018爱思唯尔出版社
For rotating machinery, early and accurate diagnosis of rotor and bearing component fault is of great significance. The classic fault diagnosis model includes two key modules, feature extraction and fault classification. In order to enhance the practicability, the deep learning models realize the end-to-end fault diagnosis by integrating this two modules, thus avoids the problems caused by the inadequate adaptability of manual designed features. However, considering the wide application scenario of fault diagnosis technology, the application scope of single deep model may have corresponding limitations. Accordingly, in this paper, an ensemble deep learning diagnosis method based on multi-objective optimization is proposed. The multi-objective optimization algorithm is used as the ensemble strategy in this method, the Convolution Residual Network (CRN), Deep Belief Network (DBN) and Deep Auto-Encoder (DAE) are weighted and integrated to realize the effective diagnosis of rotor and bearing faults for rotating machinery. The experimental results demonstrate the better adaptability of the proposed method compared to other single and ensemble deep models. (C) 2018 Published by Elsevier B.V.