Deep Learning Approach to Bearing and Induction Motor Fault Diagnosis via Data Fusion
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913890752790528 |
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| author | Sehri, Mert Ertagrin, Merve Yildirim, Ozal Orhan, Ahmet Dumond, Patrick |
| author_facet | Sehri, Mert Ertagrin, Merve Yildirim, Ozal Orhan, Ahmet Dumond, Patrick |
| contents | Convolutional Neural Networks (CNNs) are used to evaluate accelerometer and microphone data for bearing and induction motor diagnosis. A Long Short-Term Memory (LSTM) recurrent neural network is used to combine sensor information effectively, highlighting the benefits of data fusion. This approach encourages researchers to focus on multi model diagnosis for constant speed data collection by proposing a comprehensive way to use deep learning and sensor fusion and encourages data scientists to collect more multi-sensor data, including acoustic and accelerometer datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11032 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning Approach to Bearing and Induction Motor Fault Diagnosis via Data Fusion Sehri, Mert Ertagrin, Merve Yildirim, Ozal Orhan, Ahmet Dumond, Patrick Machine Learning Signal Processing Convolutional Neural Networks (CNNs) are used to evaluate accelerometer and microphone data for bearing and induction motor diagnosis. A Long Short-Term Memory (LSTM) recurrent neural network is used to combine sensor information effectively, highlighting the benefits of data fusion. This approach encourages researchers to focus on multi model diagnosis for constant speed data collection by proposing a comprehensive way to use deep learning and sensor fusion and encourages data scientists to collect more multi-sensor data, including acoustic and accelerometer datasets. |
| title | Deep Learning Approach to Bearing and Induction Motor Fault Diagnosis via Data Fusion |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2506.11032 |