State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA-BP Neural Network
Vibration analysis is considered as an effective and reliable nondestructive technique for monitoring the operation conditions of elevator control transformer. In the paper, a novel model using the Empirical Mode Decomposition (EMD), the empirical wavelet packet transform, the mind evolutionary algo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/9755094 |
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| _version_ | 1850163218097373184 |
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| author | QingHui Song QingJun Song Linjing Xiao HaiYan Jiang LiNa Li |
| author_facet | QingHui Song QingJun Song Linjing Xiao HaiYan Jiang LiNa Li |
| author_sort | QingHui Song |
| collection | DOAJ |
| description | Vibration analysis is considered as an effective and reliable nondestructive technique for monitoring the operation conditions of elevator control transformer. In the paper, a novel model using the Empirical Mode Decomposition (EMD), the empirical wavelet packet transform, the mind evolutionary algorithm (MEA), and the backpropagation (BP) neural network is proposed for elevator control transformer fault diagnosis. Firstly, the collected signal is smoothed by EMD, the intrinsic mode function (IMF) components with large noise are determined according to the correlation coefficient, the wavelet adaptive threshold denoising algorithm is used to process the noisy IMF components, and the IMF components before and after processing and its residual component are reconstructed to obtain the denoised signal. Then, the denoised signal is transformed by empirical wavelet packet transform to extract the energy ratio and energy entropy features in the wavelet packet coefficients. Finally, a fault diagnosis model composed of MEA and BP neural network is developed, which avoids the problems of premature convergence and poor diagnosis effect. The experimental results show that the proposed model has a remarkable performance with an average root mean square error of 0.00672 and the average diagnosis accuracy of 90.8%, which is better than classic BP neural network. |
| format | Article |
| id | doaj-art-ebb1f181a3214f578a4e0a0c023f4bfe |
| institution | OA Journals |
| issn | 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-ebb1f181a3214f578a4e0a0c023f4bfe2025-08-20T02:22:20ZengWileyShock and Vibration1875-92032021-01-01202110.1155/2021/9755094State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA-BP Neural NetworkQingHui Song0QingJun Song1Linjing Xiao2HaiYan Jiang3LiNa Li4Department of Mechanical and Electronic EngineeringTai-an SchoolDepartment of Mechanical and Electronic EngineeringTai-an SchoolTai-an SchoolVibration analysis is considered as an effective and reliable nondestructive technique for monitoring the operation conditions of elevator control transformer. In the paper, a novel model using the Empirical Mode Decomposition (EMD), the empirical wavelet packet transform, the mind evolutionary algorithm (MEA), and the backpropagation (BP) neural network is proposed for elevator control transformer fault diagnosis. Firstly, the collected signal is smoothed by EMD, the intrinsic mode function (IMF) components with large noise are determined according to the correlation coefficient, the wavelet adaptive threshold denoising algorithm is used to process the noisy IMF components, and the IMF components before and after processing and its residual component are reconstructed to obtain the denoised signal. Then, the denoised signal is transformed by empirical wavelet packet transform to extract the energy ratio and energy entropy features in the wavelet packet coefficients. Finally, a fault diagnosis model composed of MEA and BP neural network is developed, which avoids the problems of premature convergence and poor diagnosis effect. The experimental results show that the proposed model has a remarkable performance with an average root mean square error of 0.00672 and the average diagnosis accuracy of 90.8%, which is better than classic BP neural network.http://dx.doi.org/10.1155/2021/9755094 |
| spellingShingle | QingHui Song QingJun Song Linjing Xiao HaiYan Jiang LiNa Li State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA-BP Neural Network Shock and Vibration |
| title | State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA-BP Neural Network |
| title_full | State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA-BP Neural Network |
| title_fullStr | State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA-BP Neural Network |
| title_full_unstemmed | State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA-BP Neural Network |
| title_short | State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA-BP Neural Network |
| title_sort | state diagnosis of elevator control transformer over vibration signal based on mea bp neural network |
| url | http://dx.doi.org/10.1155/2021/9755094 |
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