4.7 Article

Adaptive Densely Connected Convolutional Auto-Encoder-Based Feature Learning of Gearbox Vibration Signals

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2020.3039612

关键词

Adaptive attention mechanism (AAM); convolutional auto-encoder (CAE); densely connected convolutional auto-encoder (DCAE); fault diagnosis; feature learning

资金

  1. National Natural Science Foundation of China [71777173]
  2. Action Plan for Scientific and Technological Innovation of Shanghai Science and Technology Commission [19511106303]
  3. Fundamental Research Funds for the Central Universities

向作者/读者索取更多资源

This article introduces a new DNN model, ADCAE, for feature extraction from vibration signals in an unsupervised way, which utilizes adaptive attention mechanism and multiscale convolution to enhance performance. Experimental results demonstrate that ADCAE performs well on gearbox vibration signals.
Vibration signals are widely utilized for machinery fault diagnosis. Typical deep neural networks (DNNs), e.g., convolutional neural networks (CNNs), perform well in feature learning from vibration signals and have been applied in gearbox fault diagnosis. However, the supervised-learning-based DNNs depend on a large amount of labeled data, and the extracted features consist of much noise and redundant information. In this article, a new DNN, adaptive densely connected convolutional auto-encoder (ADCAE), is proposed for feature extraction from 1-D vibration signals directly in an unsupervised-learning way. First, adaptive attention mechanism (AAM) is proposed for feature filtering. Second, a multiscale convolution based on AAM is proposed for fusion of multiscale information. Moreover, a new unsupervised-learning network, densely connected convolutional auto-encoder (DCAE), is further developed to improve the information flow between encoder and decoder, which significantly improves the performance of feature learning and fault classification. The results on two cases show that ADCAE has good feature extraction performance on gearbox vibration signals. It performs quite better on gearbox fault diagnosis than typical DNNs, e.g., residual CNN (ResNet), densely connected CNN (DenseNet).

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据