ArticleSensors (Basel, Switzerland)2023
Induction Motor Fault Diagnosis Using Support Vector Machine, Neural Networks, and Boosting Methods.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Advancements in Induction Motor Fault Diagnosis and Condition Monitoring: A Comprehensive Review.Sensors (Basel, Switzerland) · 2025Review
- A Review of Artificial Intelligence Techniques in Fault Diagnosis of Electric Machines.Sensors (Basel, Switzerland) · 2025Review
- A mechanism-driven failure causality modeling approach for mechanical systems combining causal ordering theory and Ishikawa diagram.Scientific reports · 2025Article
- An efficient bearing fault detection strategy based on a hybrid machine learning technique.Scientific reports · 2025Article
- Impact of Measurement Uncertainty on Fault Diagnosis Systems: A Case Study on Electrical Faults in Induction Motors.Sensors (Basel, Switzerland) · 2024Article
- Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter.Sensors (Basel, Switzerland) · 2024Article
- Induction Motor Stator Winding Inter-Tern Short Circuit Fault Detection Based on Start-Up Current Envelope Energy.Sensors (Basel, Switzerland) · 2023Article
- A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors.Sensors (Basel, Switzerland) · 2023Article
- Efficient Fault Detection of Rotor Minor Inter-Turn Short Circuit in Induction Machines Using Wavelet Transform and Empirical Mode Decomposition.Sensors (Basel, Switzerland) · 2023Article
- Design and Implementation of a Fuzzy Classifier for FDI Applied to Industrial Machinery.Sensors (Basel, Switzerland) · 2023Article
- A Support Vector Machine-Based Approach for Bolt Loosening Monitoring in Industrial Customized Vehicles.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Induction motors are robust and cost effective; thus, they are commonly used as power sources in various industrial applications. However, due to the characteristics of induction motors, industrial processes can stop when motor failures occur. Thus, research is required to realize the quick and accurate diagnosis of faults in induction motors. In this study, we constructed an induction motor simulator with normal, rotor failure, and bearing failure states. Using this simulator, 1240 vibration datasets comprising 1024 data samples were obtained for each state. Then, failure diagnosis was performed on the acquired data using support vector machine, multilayer neural network, convolutional neural network, gradient boosting machine, and XGBoost machine learning models. The diagnostic accuracies and calculation speeds of these models were verified via stratified K-fold cross validation. In addition, a graphical user interface was designed and implemented for the proposed fault diagnosis technique. The experimental results demonstrate that the proposed fault diagnosis technique is suitable for diagnosing faults in induction motors.
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