特征提取
计算机科学
卷积神经网络
人工智能
特征(语言学)
频域
断层(地质)
模式识别(心理学)
光学(聚焦)
小波
人工神经网络
原始数据
深度学习
领域知识
特征学习
试验数据
机器学习
数据挖掘
计算机视觉
光学
物理
地质学
哲学
地震学
语言学
程序设计语言
作者
Lu Jing,Ming Zhao,Ли Пин,Xiaoqiang Xu
出处
期刊:Measurement
[Elsevier]
日期:2017-12-01
卷期号:111: 1-10
被引量:511
标识
DOI:10.1016/j.measurement.2017.07.017
摘要
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.
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