Transfer Learning with Pretrained Neural Network Between Unrelated Tasks for Machine Health Diagnosis
Youssef Maher1, Boujemaa Danouj2
1Youssef Maher*, Department of Electrical and Mechanical Engineering, Univ. Hassan 1, Settat, Morocco.
2Boujemaa Danouj, Department of Electrical and Mechanical Engineering, Univ. Hassan 1, Settat, Morocco.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 2715-2720 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8489038620/2020©BEIESP | DOI: 10.35940/ijrte.F8489.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Deep Learning (DL) has contributed a lot in the field of industrial maintenance, in particular predictive maintenance by detecting potential failures and breakdowns before their appearance. Unfortunately, the DL has some limitations like the need for a large amount of data to produce an effective prediction model and also the fragility of the model in the face of changes in operating conditions. Another approach, the Transfer Learning (TL), had demonstrated in the literature that he can overcome these weaknesses. In this article, we will be using this technique with the pretrained neural network, Alex Net, which had been previously trained with the ImageNet database. Our method doesn’t require a high amount of input data and thus saves a lot of time in retraining the network in another task, which can be related or unrelated to the source task. In fact, the prediction model was successfully adapted to the bearings diagnosis case. It showed also high degree of robustness against changes of functioning conditions.
Keywords: Alex Net, Machine health diagnosis, Pretrained neural network, Transfer learning.
Scope of the Article: Deep learning.