دورية أكاديمية
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
| العنوان: | A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals |
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| المؤلفون: | Wei Zhang, Gaoliang Peng, Chuanhao Li, Yuanhang Chen, Zhujun Zhang |
| المصدر: | Sensors, Vol 17, Iss 2, p 425 (2017) |
| بيانات النشر: | MDPI AG, 2017. |
| سنة النشر: | 2017 |
| المجموعة: | LCC:Chemical technology |
| مصطلحات موضوعية: | intelligent fault diagnosis, convolutional neural networks, domain adaptation, anti-noise, Chemical technology, TP1-1185 |
| الوصف: | Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1424-8220 17020425 |
| Relation: | http://www.mdpi.com/1424-8220/17/2/425; https://doaj.org/toc/1424-8220 |
| DOI: | 10.3390/s17020425 |
| URL الوصول: | https://doaj.org/article/f0409ec90c0e484e88d5119432195b70 |
| رقم الأكسشن: | edsdoj.f0409ec90c0e484e88d5119432195b70 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14248220 17020425 |
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| DOI: | 10.3390/s17020425 |