RESEARCH PAPER
The statistical-based diagnosis with usage of acoustic sound decomposition and projected LSTM network of induction motors.
1
AGH University of Krakow, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Automatic Control and Robotics, Poland
Submission date: 2025-02-24
Final revision date: 2025-05-11
Acceptance date: 2025-05-27
Online publication date: 2025-06-05
Publication date: 2025-06-05
Corresponding author
Marek Zastępa
AGH University of Krakow, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Automatic Control and Robotics, al. A. Mickiewicza 30, 30-059, Krakow, Poland
AGH University of Krakow, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Automatic Control and Robotics, al. A. Mickiewicza 30, 30-059, Krakow, Poland
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(3):205651
HIGHLIGHTS
- A novel approach of diagnosing faults of induction motors with acoustic data features.
- The statistical parameters of IMFs are used as a features input.
- A projected LSTM model proposed for motor diagnosis.
- A fast and effective diagnosis of induction motor faults.
KEYWORDS
TOPICS
ABSTRACT
The use of acoustic signals in the diagnosis of electrical machines allows for non-invasive and rapid diagnostics. The author proposed the novel approach of acoustic diagnosis of single-phase induction motors, which is 98.67% accurate on the test set and allows for fault detection in circa 0.042 s, and 97.33% accurate for 0.021 s long samples similarly. The research includes five classes of faults. In this method, intrinsic mode functions (IMFs) gained from the empirical mode decomposition (EMD) of the motor sound are used to calculate the following statistical parameters: mean, mean square, root mean square, standard deviation, energy, and norm. Next, these parameters are organized from a prepared matrix to a vector of parameters one IMF by one, suitably for neural network input. Such prepared data is then passed to the proposed architecture of the projected LSTM neural network. The training processes were fast - they took only 12 and 13 seconds selectively. The presented novel method is useful for acoustic fault diagnosis of electric motors and could be used for other motors.
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CITATIONS (1):
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Bearing Fault Detection Using an Enhanced Multi-Reservoir Echo State Network
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