ArticleSensors (Basel, Switzerland)2020
Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- A machine learning-based classification method for SynRM faults.Scientific reports · 2026Article
- Centrifugal Pump Fault Detection with Convolutional Neural Network Transfer Learning.Sensors (Basel, Switzerland) · 2024Article
- A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft.Sensors (Basel, Switzerland) · 2023Article
- Multi-Output Classification Based on Convolutional Neural Network Model for Untrained Compound Fault Diagnosis of Rotor Systems with Non-Contact Sensors.Sensors (Basel, Switzerland) · 2023Article
- Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals.Sensors (Basel, Switzerland) · 2022Article
- Broken Bar Fault Detection Using Taylor-Fourier Filters and Statistical Analysis.Entropy (Basel, Switzerland) · 2022Article
- Monitoring Single-Phase LV Charging of Electric Vehicles.Sensors (Basel, Switzerland) · 2022Article
- An experimental method for diagnostic of incipient broken rotor bar fault in induction machines.Heliyon · 2022Article
- Adaptive Scheme for Detecting Induction Motor Incipient Broken Bar Faults at Various Load and Inertia Conditions.Sensors (Basel, Switzerland) · 2022Article
- The Use of Digital Twins in Finite Element for the Study of Induction Motors Faults.Sensors (Basel, Switzerland) · 2021Article
- Broken Rotor Bar Detection in Induction Motors through Contrast Estimation.Sensors (Basel, Switzerland) · 2021Article
- An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals.Sensors (Basel, Switzerland) · 2021Article
- A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery.Entropy (Basel, Switzerland) · 2021Article
- Mechanical Fault Diagnostic in PMSM from Only One Current Measurement: A Tacholess Order Tracking Approach.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
6 authors.
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Abstract
Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time-frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases.
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