Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks
In view of the limitations of existing rotating machine fault diagnosis methods in single-scale signal analysis, a fault diagnosis method based on multi-scale permutation entropy (MPE) and multi-channel fusion convolutional neural networks (MCFCNN) is proposed. First, MPE quantitatively analyzes the...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-07-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/22/8/851 |
| _version_ | 1797560581211815936 |
|---|---|
| author | Hongmei Li Jinying Huang Xiwang Yang Jia Luo Lidong Zhang Yu Pang |
| author_facet | Hongmei Li Jinying Huang Xiwang Yang Jia Luo Lidong Zhang Yu Pang |
| author_sort | Hongmei Li |
| collection | DOAJ |
| description | In view of the limitations of existing rotating machine fault diagnosis methods in single-scale signal analysis, a fault diagnosis method based on multi-scale permutation entropy (MPE) and multi-channel fusion convolutional neural networks (MCFCNN) is proposed. First, MPE quantitatively analyzes the vibration signals of rotating machine at different scales, and obtains permutation entropy (PE) to construct feature vector sets. Then, considering the structure and spatial information between different sensor measurement points, MCFCNN constructs multiple channels in the input layer according to the number of sensors, and each channel corresponds to the MPE feature sets of different monitored points. MCFCNN uses convolutional kernels to learn the features of each channel in an unsupervised way, and fuses the features of each channel into a new feature map. At last, multi-layer perceptron is applied to fuse multi-channel features and identify faults. Through the health monitoring experiment of planetary gearbox and rolling bearing, and compared with single channel convolutional neural networks (CNN) and existing CNN based fusion methods, the proposed method based on MPE and MCFCNN model can diagnose faults with high accuracy, stability, and speed. |
| first_indexed | 2024-03-10T18:02:46Z |
| format | Article |
| id | doaj.art-0122c692a6ba4f9dad158c5d96166a7a |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T18:02:46Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-0122c692a6ba4f9dad158c5d96166a7a2023-11-20T08:41:41ZengMDPI AGEntropy1099-43002020-07-0122885110.3390/e22080851Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural NetworksHongmei Li0Jinying Huang1Xiwang Yang2Jia Luo3Lidong Zhang4Yu Pang5School of Big data, North University of China, Taiyuan 030051, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Big data, North University of China, Taiyuan 030051, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaIn view of the limitations of existing rotating machine fault diagnosis methods in single-scale signal analysis, a fault diagnosis method based on multi-scale permutation entropy (MPE) and multi-channel fusion convolutional neural networks (MCFCNN) is proposed. First, MPE quantitatively analyzes the vibration signals of rotating machine at different scales, and obtains permutation entropy (PE) to construct feature vector sets. Then, considering the structure and spatial information between different sensor measurement points, MCFCNN constructs multiple channels in the input layer according to the number of sensors, and each channel corresponds to the MPE feature sets of different monitored points. MCFCNN uses convolutional kernels to learn the features of each channel in an unsupervised way, and fuses the features of each channel into a new feature map. At last, multi-layer perceptron is applied to fuse multi-channel features and identify faults. Through the health monitoring experiment of planetary gearbox and rolling bearing, and compared with single channel convolutional neural networks (CNN) and existing CNN based fusion methods, the proposed method based on MPE and MCFCNN model can diagnose faults with high accuracy, stability, and speed.https://www.mdpi.com/1099-4300/22/8/851multiscale permutation entropyinformation fusionmulti-channelconvolutional neural networksfault diagnosisrotating machinery |
| spellingShingle | Hongmei Li Jinying Huang Xiwang Yang Jia Luo Lidong Zhang Yu Pang Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks Entropy multiscale permutation entropy information fusion multi-channel convolutional neural networks fault diagnosis rotating machinery |
| title | Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks |
| title_full | Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks |
| title_fullStr | Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks |
| title_full_unstemmed | Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks |
| title_short | Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks |
| title_sort | fault diagnosis for rotating machinery using multiscale permutation entropy and convolutional neural networks |
| topic | multiscale permutation entropy information fusion multi-channel convolutional neural networks fault diagnosis rotating machinery |
| url | https://www.mdpi.com/1099-4300/22/8/851 |
| work_keys_str_mv | AT hongmeili faultdiagnosisforrotatingmachineryusingmultiscalepermutationentropyandconvolutionalneuralnetworks AT jinyinghuang faultdiagnosisforrotatingmachineryusingmultiscalepermutationentropyandconvolutionalneuralnetworks AT xiwangyang faultdiagnosisforrotatingmachineryusingmultiscalepermutationentropyandconvolutionalneuralnetworks AT jialuo faultdiagnosisforrotatingmachineryusingmultiscalepermutationentropyandconvolutionalneuralnetworks AT lidongzhang faultdiagnosisforrotatingmachineryusingmultiscalepermutationentropyandconvolutionalneuralnetworks AT yupang faultdiagnosisforrotatingmachineryusingmultiscalepermutationentropyandconvolutionalneuralnetworks |