Image-Processing-Based Intelligent Defect Diagnosis of Rolling Element Bearings Using Spectrogram Images
Due to the excellent image recognition characteristics of convolutional neural networks (CNN), they have gained significant attention among researchers for image-processing-based defect diagnosis tasks. The use of deep CNN models for rolling element bearings’ (REBs’) defect diagnosis may be computat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/10/10/908 |
| _version_ | 1797471944823537664 |
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| author | Syed Muhammad Tayyab Steven Chatterton Paolo Pennacchi |
| author_facet | Syed Muhammad Tayyab Steven Chatterton Paolo Pennacchi |
| author_sort | Syed Muhammad Tayyab |
| collection | DOAJ |
| description | Due to the excellent image recognition characteristics of convolutional neural networks (CNN), they have gained significant attention among researchers for image-processing-based defect diagnosis tasks. The use of deep CNN models for rolling element bearings’ (REBs’) defect diagnosis may be computationally expensive, and therefore may not be suitable for some applications where hardware and resources limitations exist. However, instead of using CNN models as end-to-end image classifiers, they can also be used to extract the deep features from images and those features can further be used as input to machine learning (ML) models for defect diagnosis tasks. In addition to extracting deep features using CNN models, there are also other methods for feature extraction from vibration characteristic images, such as the extraction of handcrafted features using the histogram of oriented gradients (HOG) and local binary pattern (LBP) descriptors. These features can also be used as input to classical ML models for image classification tasks. In this study, a performance comparison between all these image-processing-based defect diagnosis techniques was carried out in terms of fault detection accuracy and computational expense. Moreover, based upon the detailed comparison, a hybrid-ensemble method involving decision-level fusion is proposed, which is far less computationally expensive compared to CNN models while using them as end-to-end classifiers. The performance of all these models is also compared in the case of minimal training data availability and for diagnosis under slightly different operating conditions to ascertain their generalizability and ability to correctly diagnose despite the minimal availability of training data. The performance of the proposed hybrid-ensemble method remained outstanding for the REBs’ defect diagnosis despite the minimal of availability training data as well as the slight variation under operating conditions. |
| first_indexed | 2024-03-09T19:55:16Z |
| format | Article |
| id | doaj.art-9f267f9601564039b1a89a455f3c58f5 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T19:55:16Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-9f267f9601564039b1a89a455f3c58f52023-11-24T00:59:31ZengMDPI AGMachines2075-17022022-10-01101090810.3390/machines10100908Image-Processing-Based Intelligent Defect Diagnosis of Rolling Element Bearings Using Spectrogram ImagesSyed Muhammad Tayyab0Steven Chatterton1Paolo Pennacchi2Department of Mechanical Engineering, Politecnico di Milano, Via G. la Masa 1, 20156 Milan, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Via G. la Masa 1, 20156 Milan, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Via G. la Masa 1, 20156 Milan, ItalyDue to the excellent image recognition characteristics of convolutional neural networks (CNN), they have gained significant attention among researchers for image-processing-based defect diagnosis tasks. The use of deep CNN models for rolling element bearings’ (REBs’) defect diagnosis may be computationally expensive, and therefore may not be suitable for some applications where hardware and resources limitations exist. However, instead of using CNN models as end-to-end image classifiers, they can also be used to extract the deep features from images and those features can further be used as input to machine learning (ML) models for defect diagnosis tasks. In addition to extracting deep features using CNN models, there are also other methods for feature extraction from vibration characteristic images, such as the extraction of handcrafted features using the histogram of oriented gradients (HOG) and local binary pattern (LBP) descriptors. These features can also be used as input to classical ML models for image classification tasks. In this study, a performance comparison between all these image-processing-based defect diagnosis techniques was carried out in terms of fault detection accuracy and computational expense. Moreover, based upon the detailed comparison, a hybrid-ensemble method involving decision-level fusion is proposed, which is far less computationally expensive compared to CNN models while using them as end-to-end classifiers. The performance of all these models is also compared in the case of minimal training data availability and for diagnosis under slightly different operating conditions to ascertain their generalizability and ability to correctly diagnose despite the minimal availability of training data. The performance of the proposed hybrid-ensemble method remained outstanding for the REBs’ defect diagnosis despite the minimal of availability training data as well as the slight variation under operating conditions.https://www.mdpi.com/2075-1702/10/10/908rolling element bearingsimage processingintelligent defect diagnosisartificial neural network (ANN)convolutional neural network (CNN)K-nearest neighbor (KNN) |
| spellingShingle | Syed Muhammad Tayyab Steven Chatterton Paolo Pennacchi Image-Processing-Based Intelligent Defect Diagnosis of Rolling Element Bearings Using Spectrogram Images Machines rolling element bearings image processing intelligent defect diagnosis artificial neural network (ANN) convolutional neural network (CNN) K-nearest neighbor (KNN) |
| title | Image-Processing-Based Intelligent Defect Diagnosis of Rolling Element Bearings Using Spectrogram Images |
| title_full | Image-Processing-Based Intelligent Defect Diagnosis of Rolling Element Bearings Using Spectrogram Images |
| title_fullStr | Image-Processing-Based Intelligent Defect Diagnosis of Rolling Element Bearings Using Spectrogram Images |
| title_full_unstemmed | Image-Processing-Based Intelligent Defect Diagnosis of Rolling Element Bearings Using Spectrogram Images |
| title_short | Image-Processing-Based Intelligent Defect Diagnosis of Rolling Element Bearings Using Spectrogram Images |
| title_sort | image processing based intelligent defect diagnosis of rolling element bearings using spectrogram images |
| topic | rolling element bearings image processing intelligent defect diagnosis artificial neural network (ANN) convolutional neural network (CNN) K-nearest neighbor (KNN) |
| url | https://www.mdpi.com/2075-1702/10/10/908 |
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