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Electrical Engineering
Published in:

03-05-2024 | Original Paper

Mechanical and electrical faults detection in induction motor across multiple sensors with CNN-LSTM deep learning model

Authors: Merve Ertargin, Ozal Yildirim, Ahmet Orhan

Published in: Electrical Engineering | Issue 6/2024

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Abstract

The article presents a novel CNN-LSTM deep learning model for detecting mechanical and electrical faults in induction motors using vibration data from multiple sensors. The model, which combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks, captures both spatial and temporal features of the data. The study uses the UOEMD-VAFCVS dataset, which includes vibration signals from three different accelerometers positioned on an induction motor and its bearings. The proposed model achieved high accuracy rates, demonstrating its robustness and generalizability across different sensor data. The authors also compare the performance of the CNN-LSTM model with other architectures, such as 13-layer and 15-layer CNN models, showing that the CNN-LSTM model outperforms them in terms of accuracy and stability. The study highlights the potential of deep learning models in improving the reliability and efficiency of industrial processes by enabling proactive maintenance and reducing downtime.
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Metadata
Title
Mechanical and electrical faults detection in induction motor across multiple sensors with CNN-LSTM deep learning model
Authors
Merve Ertargin
Ozal Yildirim
Ahmet Orhan
Publication date
03-05-2024
Publisher
Springer Berlin Heidelberg
Published in
Electrical Engineering / Issue 6/2024
Print ISSN: 0948-7921
Electronic ISSN: 1432-0487
DOI
https://doi.org/10.1007/s00202-024-02420-w

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