Sustainable Energy and Artificial Intelligence

A Low Cost IoT-Based Hybrid Multiscale CNN-LSTM Approach for Bearing Fault Diagnosis Using Low Sampling Rate Vibration Data

Document Type : Original Article

Authors

Seyed Mohammad Mahdi Moosavi 1
Sajad Khoshbakht 1
Hossein Taheri 2

1 Electrical and Computer Engineering Department, Hamedan University of Technology, Hamedan, Iran

2 Department of Manufacturing Engineering, Georgia Southern University

https://doi.org/10.61186/seai.2409-1005
Abstract
Electric motors are vital in energy systems for converting energy efficiently. Their reliability ensures consistent performance, making them indispensable in renewable energy applications and industrial processes. Bearings are crucial for motor operation, yet they are vulnerable to faults that can impair performance and reduce lifespan. Conventional fault detection methods require high sampling rate vibration data and expensive sensors. This paper presents a cost-effective solution by introducing a hybrid model combining Multiscale Convolutional Neural Networks (MSCNN) with Long Short-Term Memory (LSTM) networks for bearing fault diagnosis using low sampling rate data. We developed dedicated IoT-based hardware and a web server for real-time monitoring. Our approach leverages MSCNN for spatial feature extraction and LSTM for temporal pattern recognition, achieving high diagnostic accuracy with lower resolution data. Experimental results show that our MSCNN-LSTM model provides diagnostic accuracy comparable to or surpassing that of high sampling rate methods, offering a robust and economical solution for bearing fault detection.

Highlights

  • Developed an IoT-based fault diagnosis system utilizing low sampling rate vibration data.
  • Proposed a robust hybrid CNN-LSTM model for accurate and efficient fault detection.
  • Achieved cost-effective, real-time monitoring for industrial bearing systems.
  • Enhanced fault diagnosis accuracy while reducing hardware and maintenance costs.
  • Improved equipment reliability through early fault detection.

Keywords

Multiscale Fault Diagnosis
Convolutional Neural Network
Long Short-Term Memory
Bearing Fault, Internet of Things

Subjects

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Sustainable Energy and Artificial Intelligence
Volume 1, Issue 2
Spring 2025
Pages 113-125

  • Receive Date 07 September 2024
  • Revise Date 14 September 2024
  • Accept Date 17 September 2024
  • First Publish Date 28 September 2024

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