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All Journal Specta Journal of Technology
Min-Fu Hsieh
National Cheng Kung University (NCKU)
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Environmental Science

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Effectiveness of the Number of Extracted Features from MFCC on Deep Learning for Fault Classification of Industrial Machinery Based on Acoustic Signal: 283 - 293 Thorikul Huda; Min-Fu Hsieh; Faaris Mujaahid; Muhammad Rhido Dewanto
SPECTA Journal of Technology Vol. 10 No. 2 (2026): Specta Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35718/specta.v10i2.1344

Abstract

Reliable and efficient fault classification of induction motors is a critical concern in industrial environments. This study focuses on four operational states of a three-phase induction motor: normal function, imbalance fault, and horizontal and vertical misalignment faults. We propose a comparative analysis of the impact of the number of features extracted from Mel-frequency Cepstral Coefficient (MFCC) for reliable fault diagnosis of induction motors using acoustic emission signal processing. In this work, Long Short-Term Memory (LSTM) is utilized as the fault classification algorithm, and its performance is compared with three other deep learning models: Feed-Forward Neural Network (FFNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). The results demonstrate that using LSTM with 13 MFCC features achieves a validation accuracy of approximately 97.4%, with 26 MFCC features achieving 96.12%, and with 39 MFCC features achieving 96.3%.
Co-Authors Faaris Mujaahid Muhammad Rhido Dewanto Thorikul Huda