دورية أكاديمية
Intelligent Fault Diagnosis of Broken Wires for Steel Wire Ropes Based on Generative Adversarial Nets
| العنوان: | Intelligent Fault Diagnosis of Broken Wires for Steel Wire Ropes Based on Generative Adversarial Nets |
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| المؤلفون: | Yiqing Zhang, Jialin Han, Luyang Jing, Chengming Wang, Ling Zhao |
| المصدر: | Applied Sciences, Vol 12, Iss 22, p 11552 (2022) |
| بيانات النشر: | MDPI AG, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| مصطلحات موضوعية: | fault diagnosis, steel wire rope, broken wire, generative adversarial nets, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| الوصف: | The quantitative identification of broken wires is of great significance to maintain the safety of mechanical systems, such as steel wire ropes. However, in order to achieve high accuracy recognition results, a large number of fault samples are necessary, which is difficult to achieve in practical industrial detection. In this paper, a novel quantitative identification approach, based on generative adversarial nets (GANs) and a convolutional neural network (CNN), is proposed to solve the broken wire recognition problem in situations where real inspections have generated only a small sample of broken wires for analysis. One-dimensional original signals of broken wires are transformed into two-dimensional time-frequency images by continuous wavelet transform (CWT). Next, these time-frequency images are used for quantitative identification of various defects by combing GANs and CNN with limited samples. The main innovation of this paper is that the identification accuracy of broken wires can be improved by generating fault samples through GANs. The experimental results demonstrate that the proposed method achieves better recognition rates for broken wires compared with the existing detection methods. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 12221155 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/12/22/11552; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app122211552 |
| URL الوصول: | https://doaj.org/article/04183e9b27824e06a10ffbbde7053d1d |
| رقم الأكسشن: | edsdoj.04183e9b27824e06a10ffbbde7053d1d |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 12221155 20763417 |
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| DOI: | 10.3390/app122211552 |