Optimization of Sample Size, Data Points, and Data Augmentation Stride in Vibration Signal Analysis for Deep Learning-Based Fault Diagnosis of Rotating Machines
In recent years, deep learning models have increasingly been employed for fault diagnosis in rotating machines, with remarkable results. However, the accuracy and reliability of these models in fault diagnosis tasks can be significantly influenced by critical input parameters, such as the sample siz...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/vib/5590157 |
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| _version_ | 1823859650567602176 |
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| author | Fasikaw Kibrete Dereje Engida Woldemichael Hailu Shimels Gebremedhen |
| author_facet | Fasikaw Kibrete Dereje Engida Woldemichael Hailu Shimels Gebremedhen |
| author_sort | Fasikaw Kibrete |
| collection | DOAJ |
| description | In recent years, deep learning models have increasingly been employed for fault diagnosis in rotating machines, with remarkable results. However, the accuracy and reliability of these models in fault diagnosis tasks can be significantly influenced by critical input parameters, such as the sample size, the number of data points within each sample, and the augmentation stride in vibration signal analysis. To address this challenge, this paper proposes a new adaptive method based on Bayesian optimization to determine the optimal combination of these input parameters from raw vibration signals and enhance the diagnostic performance of deep learning models. This study utilizes a one-dimensional convolutional neural network (1-D CNN) as the deep learning model for fault classification. The proposed adaptive 1-D CNN-based fault diagnosis method is validated via vibration signals collected from motor rolling bearings and achieves a fault diagnosis accuracy of 100%. Compared with existing CNN-based diagnosis methods, this adaptive approach not only achieves the highest accuracy on the testing set but also demonstrates stable performance during training, even under varying operating conditions. These results indicate the importance of optimizing the input parameters of deep learning models employed in fault diagnosis tasks. |
| format | Article |
| id | doaj-art-e3b7c5264d474f14ba3fac8c9be93eda |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-e3b7c5264d474f14ba3fac8c9be93eda2025-02-11T00:00:03ZengWileyShock and Vibration1875-92032025-01-01202510.1155/vib/5590157Optimization of Sample Size, Data Points, and Data Augmentation Stride in Vibration Signal Analysis for Deep Learning-Based Fault Diagnosis of Rotating MachinesFasikaw Kibrete0Dereje Engida Woldemichael1Hailu Shimels Gebremedhen2Department of Mechanical EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringIn recent years, deep learning models have increasingly been employed for fault diagnosis in rotating machines, with remarkable results. However, the accuracy and reliability of these models in fault diagnosis tasks can be significantly influenced by critical input parameters, such as the sample size, the number of data points within each sample, and the augmentation stride in vibration signal analysis. To address this challenge, this paper proposes a new adaptive method based on Bayesian optimization to determine the optimal combination of these input parameters from raw vibration signals and enhance the diagnostic performance of deep learning models. This study utilizes a one-dimensional convolutional neural network (1-D CNN) as the deep learning model for fault classification. The proposed adaptive 1-D CNN-based fault diagnosis method is validated via vibration signals collected from motor rolling bearings and achieves a fault diagnosis accuracy of 100%. Compared with existing CNN-based diagnosis methods, this adaptive approach not only achieves the highest accuracy on the testing set but also demonstrates stable performance during training, even under varying operating conditions. These results indicate the importance of optimizing the input parameters of deep learning models employed in fault diagnosis tasks.http://dx.doi.org/10.1155/vib/5590157 |
| spellingShingle | Fasikaw Kibrete Dereje Engida Woldemichael Hailu Shimels Gebremedhen Optimization of Sample Size, Data Points, and Data Augmentation Stride in Vibration Signal Analysis for Deep Learning-Based Fault Diagnosis of Rotating Machines Shock and Vibration |
| title | Optimization of Sample Size, Data Points, and Data Augmentation Stride in Vibration Signal Analysis for Deep Learning-Based Fault Diagnosis of Rotating Machines |
| title_full | Optimization of Sample Size, Data Points, and Data Augmentation Stride in Vibration Signal Analysis for Deep Learning-Based Fault Diagnosis of Rotating Machines |
| title_fullStr | Optimization of Sample Size, Data Points, and Data Augmentation Stride in Vibration Signal Analysis for Deep Learning-Based Fault Diagnosis of Rotating Machines |
| title_full_unstemmed | Optimization of Sample Size, Data Points, and Data Augmentation Stride in Vibration Signal Analysis for Deep Learning-Based Fault Diagnosis of Rotating Machines |
| title_short | Optimization of Sample Size, Data Points, and Data Augmentation Stride in Vibration Signal Analysis for Deep Learning-Based Fault Diagnosis of Rotating Machines |
| title_sort | optimization of sample size data points and data augmentation stride in vibration signal analysis for deep learning based fault diagnosis of rotating machines |
| url | http://dx.doi.org/10.1155/vib/5590157 |
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