Motor Fault Diagnosis and Detection with Convolutional Autoencoder (CAE) Based on Analysis of Electrical Energy Data

This study develops a Convolutional Autoencoder (CAE) and deep neural network (DNN)-based model optimized for real-time signal processing and high accuracy in motor fault diagnosis. This model learns complex patterns from voltage and current data and precisely analyzes them in combination with DNN t...

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Veröffentlicht in:Electronics (Basel) Jg. 13; H. 19; S. 3946
Hauptverfasser: Choi, YuRim, Joe, Inwhee
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2024
Schlagworte:
Accuracy
Artificial intelligence
Artificial neural networks
Data analysis
Data processing
Data transmission
Deep learning
Efficiency
Electric motors
Electric potential
Electric power systems
Energy consumption
Fault diagnosis
Harmonic distortion
Image processing
Industrial applications
Information management
Integrated approach
Knowledge representation
Machine learning
Neural networks
Pattern recognition
Preventive maintenance
Productivity
Real time
Sensors
Signal processing
Systems analysis
Vibration analysis
Vibration monitoring
Voltage
ISSN:2079-9292, 2079-9292
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Zusammenfassung:This study develops a Convolutional Autoencoder (CAE) and deep neural network (DNN)-based model optimized for real-time signal processing and high accuracy in motor fault diagnosis. This model learns complex patterns from voltage and current data and precisely analyzes them in combination with DNN through latent space representation. Traditional diagnostic methods relied on vibration and current sensors, empirical knowledge, or harmonic and threshold-based monitoring, but they had limitations in recognizing complex patterns and providing accurate diagnoses. Our model significantly enhances the accuracy of power data analysis and fault diagnosis by mapping each phase (R, S, and T) of the electrical system to the red, green, and blue (RGB) channels of image processing and applying various signal processing techniques. Optimized for real-time data streaming, this model demonstrated high practicality and effectiveness in an actual industrial environment, achieving 99.9% accuracy, 99.8% recall, and 99.9% precision. Specifically, it was able to more accurately diagnose motor efficiency and fault risks by utilizing power system analysis indicators such as phase voltage, total harmonic distortion (THD), and voltage unbalance. This integrated approach significantly enhances the real-time applicability of electric motor fault diagnosis and is expected to provide a crucial foundation for various industrial applications in the future.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13193946