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DOI: 10.14569/IJACSA.2022.0130424
PDF

Bearing Fault Detection based on Internet of Things using Convolutional Neural Network

Author 1: Sovon Chakraborty
Author 2: F. M. Javed Mehedi Shamrat
Author 3: Rasel Ahammad
Author 4: Md. Masum Billah
Author 5: Moumita Kabir
Author 6: Md Rabbani Hosen

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 4, 2022.

  • Abstract and Keywords
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Abstract: In the age of the industrial revolution, industry and machinery are elements of the utmost importance to the development of human civilization. As industries are dependent on their machines, regular maintenance of these machines is required. However, if the machine is too big for humans to look after, we need a system that will observe these giants. This paper proposes a convolutional neural network-based system that detects faults in industrial machines by diagnosing motor sounds using accelerometers sensors. The sensors collect data from the machines and augment the data into 261756 samples to train (70%) and test (30%) the models for better accuracy. The sensor data are sent to the server through the wireless sensor network and decomposed using discrete wavelet transformation (DWT). This big data is processed to detect faults. The study shows that custom CNN architectures surpass the performance of the transfer learning-based MobileNetV2 fault diagnosis model. The system could successfully detect faults with up to 99.64% accuracy and 99.83% precision with the MobileNetV2 pre-trained on the ImageNet Dataset. However, the Convolutional 1D and 2D architectures perform excellently with 100% accuracy and 100 % precision.

Keywords: Accuracy; convolution 1D; convolution 2D; data loss; faulty machinery; mobileNetV2; precision

Sovon Chakraborty, F. M. Javed Mehedi Shamrat, Rasel Ahammad, Md. Masum Billah, Moumita Kabir and Md Rabbani Hosen, “Bearing Fault Detection based on Internet of Things using Convolutional Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 13(4), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130424

@article{Chakraborty2022,
title = {Bearing Fault Detection based on Internet of Things using Convolutional Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130424},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130424},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {4},
author = {Sovon Chakraborty and F. M. Javed Mehedi Shamrat and Rasel Ahammad and Md. Masum Billah and Moumita Kabir and Md Rabbani Hosen}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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