Machine fault diagnosis using a modified transferable CNN
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023.
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| Idioma: | English |
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Brac University
2024
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| Acesso em linha: | http://hdl.handle.net/10361/22650 |
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10361-226502024-04-23T21:04:36Z Machine fault diagnosis using a modified transferable CNN Shammi, Sanjana Khan Chakraborty, Amitabha Department of Computer Science and Engineering, Brac University Transfer learning CNN ResNet18 Leaky ReLU XAI SqueezeNet Fault location (Engineering)--Automation. Machine learning. This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 61-65). Detecting prior bearing faults is an essential task of machine health monitoring because bearings are the crucial parts of rotating machines. The performance of traditional intelligent fault diagnosis methods depends on feature extraction of fault signals, which requires signal processing techniques, expert knowledge, and human labor. Deep learning algorithms have recently been applied for industrial machine health monitoring with their advanced features. With the capacity to automatically learn complex features of input data, deep learning architectures have great potential to overcome the drawbacks of traditional intelligent fault diagnosis. This paper proposes a rolling bearing fault diagnosis method based on Convolutional Neural Network and Leaky ReLU to solve the above problems. Firstly, the Continuous Wavelet Transform converts one-dimensional original vibration signals into two-dimensional time-frequency images. Secondly, the obtained time-frequency images are used to train the constructed model. Finally, the diagnosis of the fault location and severity is completed. The method is verified on the MFPT, MIMII data set, and vehicle engine. The results demonstrate that the suggested approach achieves higher diagnostic accuracy which is 95.49% on average and 2% greater than other advanced techniques. We have also incorporated XAI in the input images to make the network more transparent. Sanjana Khan Shammi M.Sc. in Computer Science and Engineering 2024-04-23T05:07:47Z 2024-04-23T05:07:47Z ©2023 2023-07 Thesis ID: 19166014 http://hdl.handle.net/10361/22650 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 66 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Transfer learning CNN ResNet18 Leaky ReLU XAI SqueezeNet Fault location (Engineering)--Automation. Machine learning. |
| spellingShingle |
Transfer learning CNN ResNet18 Leaky ReLU XAI SqueezeNet Fault location (Engineering)--Automation. Machine learning. Shammi, Sanjana Khan Machine fault diagnosis using a modified transferable CNN |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023. |
| author2 |
Chakraborty, Amitabha |
| author_facet |
Chakraborty, Amitabha Shammi, Sanjana Khan |
| format |
Thesis |
| author |
Shammi, Sanjana Khan |
| author_sort |
Shammi, Sanjana Khan |
| title |
Machine fault diagnosis using a modified transferable CNN |
| title_short |
Machine fault diagnosis using a modified transferable CNN |
| title_full |
Machine fault diagnosis using a modified transferable CNN |
| title_fullStr |
Machine fault diagnosis using a modified transferable CNN |
| title_full_unstemmed |
Machine fault diagnosis using a modified transferable CNN |
| title_sort |
machine fault diagnosis using a modified transferable cnn |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22650 |
| work_keys_str_mv |
AT shammisanjanakhan machinefaultdiagnosisusingamodifiedtransferablecnn |
| _version_ |
1814309815222009856 |