Hybrid CNN-LSTM Model for Non-Invasive Fault Detection in Induction Motors Using Acoustic Data
Seralieva A. Ilyas M. Gissa A. Ali M.H.
2024Institute of Electrical and Electronics Engineers Inc.
Proceedings of the IEEE Conference on Systems, Process and Control, ICSPC
2024Issue 2024136 - 141 pp.
Monitoring the health condition of industrial drives, especially, induction motors is crucial to maintaining the reliability and efficiency of industrial processes. Fault detection techniques tend to be invasive and expensive despite their effectiveness. This paper introduces a non-invasive methodology that leverages acoustic signals processed through sophisticated deep learning models, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs). We aim to accurately detect and classify different fault conditions in induction motors by extracting frequency features from acoustic data. Our results show marked improvements over conventional methods, offering a valuable predictive non-invasive maintenance tool.
convolutional neural network , deep learning , fault detection , industrial drives , Long Short-Term Memory networks
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Kazakh-British Technical University, School of IT and Engineering, Almaty, 050000, Kazakhstan
SEDS Nazarbayev University, Dept. of Mechanical and Aerospace Engineering, Astana, 010000, Kazakhstan
Kazakh-British Technical University
SEDS Nazarbayev University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026