Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy Environments
With the development of modern military technology, electrical drive technology has become a power source for modern artillery. In fault monitoring of a driving motor mounted on a piece of artillery, various sensors are susceptible to interference from the complex environment, both inside and outsid...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/3/847 |
| _version_ | 1797318271218745344 |
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| author | Wenkuan Huang Yong Li Jinsong Tang Linfang Qian |
| author_facet | Wenkuan Huang Yong Li Jinsong Tang Linfang Qian |
| author_sort | Wenkuan Huang |
| collection | DOAJ |
| description | With the development of modern military technology, electrical drive technology has become a power source for modern artillery. In fault monitoring of a driving motor mounted on a piece of artillery, various sensors are susceptible to interference from the complex environment, both inside and outside the artillery itself. In this study, we creatively propose a fault diagnosis model based on an attention mechanism, the AdaBoost method and a wavelet noise reduction network to address the difficulty in obtaining high-quality motor signals in complex noisy interference environments. First, multiple fusion wavelet basis, soft thresholding, and index soft filter optimization were used to train multiple wavelet noise reduction networks that could recover sample signals under different noise conditions. Second, a convolutional neural network (CNN) classification module was added to construct end-to-end classification models that could correctly identify faults. The above basis classification models were then integrated into the AdaBoost method with an improved attention mechanism to develop a fault diagnosis model suitable for complex noisy environments. Finally, two experiments were conducted to validate the proposed method. Under motor signals with varying signal-to-noise ratios (SNRs) noises, the proposed method achieved an average accuracy of 92%, surpassing the conventional method by over 8.5%. |
| first_indexed | 2024-03-08T03:48:59Z |
| format | Article |
| id | doaj.art-a0acecd5f8844e9d89cf67d448d70c33 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T03:48:59Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a0acecd5f8844e9d89cf67d448d70c332024-02-09T15:22:01ZengMDPI AGSensors1424-82202024-01-0124384710.3390/s24030847Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy EnvironmentsWenkuan Huang0Yong Li1Jinsong Tang2Linfang Qian3School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaWith the development of modern military technology, electrical drive technology has become a power source for modern artillery. In fault monitoring of a driving motor mounted on a piece of artillery, various sensors are susceptible to interference from the complex environment, both inside and outside the artillery itself. In this study, we creatively propose a fault diagnosis model based on an attention mechanism, the AdaBoost method and a wavelet noise reduction network to address the difficulty in obtaining high-quality motor signals in complex noisy interference environments. First, multiple fusion wavelet basis, soft thresholding, and index soft filter optimization were used to train multiple wavelet noise reduction networks that could recover sample signals under different noise conditions. Second, a convolutional neural network (CNN) classification module was added to construct end-to-end classification models that could correctly identify faults. The above basis classification models were then integrated into the AdaBoost method with an improved attention mechanism to develop a fault diagnosis model suitable for complex noisy environments. Finally, two experiments were conducted to validate the proposed method. Under motor signals with varying signal-to-noise ratios (SNRs) noises, the proposed method achieved an average accuracy of 92%, surpassing the conventional method by over 8.5%.https://www.mdpi.com/1424-8220/24/3/847fault diagnosisartillery driving motorattention mechanismAdaBoostnoise |
| spellingShingle | Wenkuan Huang Yong Li Jinsong Tang Linfang Qian Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy Environments Sensors fault diagnosis artillery driving motor attention mechanism AdaBoost noise |
| title | Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy Environments |
| title_full | Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy Environments |
| title_fullStr | Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy Environments |
| title_full_unstemmed | Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy Environments |
| title_short | Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy Environments |
| title_sort | fault diagnosis methods for an artillery loading system driving motor in complex noisy environments |
| topic | fault diagnosis artillery driving motor attention mechanism AdaBoost noise |
| url | https://www.mdpi.com/1424-8220/24/3/847 |
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