A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox
A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox
复制标题
基于卷积神经网络的齿轮箱状态监测特征学习与故障诊断方法
DOI：
10.1016/j.measurement.2017.07.017
复制
发表时间：
2017-12-01
期刊：
影响因子：
5.6
通讯作者：
Xu, Xiaoqiang
中科院分区：
文献类型：
--
作者：
Jing, Luyang;Zhao, Ming;Xu, Xiaoqiang
特征提取在机械系统智能故障诊断中起着至关重要的作用。然而，传统的特征提取方法存在三个问题：（1）对领域专家和先验知识的要求;（2）对机械系统的变化敏感;（3）挖掘新特征的局限性。研究一种能够自适应地从原始数据中学习特征并发现新的故障敏感特征的自动特征提取方法具有重要意义。深度学习已广泛应用于图像分析和语音识别，并取得了巨大成功。该方法的主要优点在于能够从原始数据中挖掘出具有代表性的信息和敏感特征。然而，深度学习在机械诊断特征学习中的应用仍然很少，并且已经进行了有限的研究来比较不同数据类型的特征学习的有效性。本文将重点开发一种卷积神经网络（CNN），直接从振动信号的频率数据中学习特征，并测试从原始数据，频谱和组合时频数据中学习特征的不同性能。分别从时域、频域和小波域三个方面对人工识别方法和三种常用的智能识别方法进行了比较。通过PHM 2009齿轮箱挑战数据和行星齿轮箱试验台验证了该方法的有效性。结果表明，该方法能够从频率数据中自适应地学习特征，并获得比其他比较方法更高的诊断精度。(C)2017爱思唯尔有限公司版权所有
Feature extraction plays a vital role in intelligent fault diagnosis of mechanical system. Nevertheless, traditional feature extraction methods suffer from three problems, which are (1) the requirements of domain expertise and prior knowledge, (2) the sensitive to the changes of mechanical system and (3) the limitations of mining new features. It is attractive and meaningful to investigate an automatic feature extraction method, which can adaptively learn features from raw data and discover new fault-sensitive features. Deep learning has been widely used in image analysis and speech recognition with great success. The key advantage of this method lies into the ability of mining representative information and sensitive features from raw data. However, the application of deep learning in feature leaning for mechanical diagnosis is still few, and limited studies have been carried out to compare the effectiveness of feature leaning with various data types. This paper will focus on developing a convolutional neural network (CNN) to learn features directly from frequency data of vibration signals and testing the different performance of feature learning from raw data, frequency spectrum and combined time-frequency data. Manual features from time domain, frequency domain and wavelet domain as well as three common intelligent methods are used as comparisons. The effectiveness of the proposed method is validated through PHM 2009 gearbox challenge data and a planetary gearbox test rig. The results demonstrate that the proposed method is able to learn features adaptively from frequency data and achieve higher diagnosis accuracy than other comparative methods. (C) 2017 Elsevier Ltd. All rights reserved.