دورية أكاديمية
Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection
| العنوان: | Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection |
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| المؤلفون: | Dongying Han, Kai Liang, Peiming Shi |
| المصدر: | Journal of Low Frequency Noise, Vibration and Active Control, Vol 39 (2020) |
| بيانات النشر: | SAGE Publishing, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Acoustics. Sound |
| مصطلحات موضوعية: | Control engineering systems. Automatic machinery (General), TJ212-225, Acoustics. Sound, QC221-246 |
| الوصف: | In the absence of a priori knowledge, manual feature selection is too blind to find the sensitive features which can effectively classify the different fault features. And it is difficult to obtain a large number of typical fault samples in practice to train the intelligent classifier. A novel intelligent fault diagnosis method based on feature selection and deep learning is proposed for rotating machine mechanical in the paper. In this method, the deep neural network is not only used for feature extraction but also for fault diagnosis. First, the deep neural network 1 is used to extract feature from the spectral signal of the original signal. In addition, the original vibration signal is decomposed to a series of intrinsic mode function components by empirical mode decomposition, and the statistical features of each intrinsic mode function component are extracted by the deep neural network 2 in time domain and frequency domain. Second, the extraction features of the original signal spectrum and the extraction features of each intrinsic mode function component are evaluated, respectively. After features evaluation, the selected sensitive features are combined together to construct a joint feature. Finally, the joint feature is put into the deep neural network 3 to realize the automatic recognition of different fault states of rotating machinery. The experimental results show that the method proposed in this paper which integrated time-domain, frequency-domain statistical characteristics, empirical mode decomposition, feature selection, and deep learning methods can obtain the fault information in detail and can select sensitive features from a large number of fault features. The method can reduce the network size, improve the mechanical fault diagnosis classification accuracy, and has strong robustness. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1461-3484 2048-4046 14613484 |
| Relation: | https://doaj.org/toc/1461-3484; https://doaj.org/toc/2048-4046 |
| DOI: | 10.1177/1461348419849279 |
| URL الوصول: | https://doaj.org/article/a7bb26b37ab94c7390d46bd6416dd13a |
| رقم الأكسشن: | edsdoj.7bb26b37ab94c7390d46bd6416dd13a |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14613484 20484046 |
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| DOI: | 10.1177/1461348419849279 |