ArticleSensors (Basel, Switzerland)2022
Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Advancements in Induction Motor Fault Diagnosis and Condition Monitoring: A Comprehensive Review.Sensors (Basel, Switzerland) · 2025Review
- Vibration-Based Anomaly Detection for Induction Motors Using Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis.Sensors (Basel, Switzerland) · 2025Article
- Machine learning based fault classification for improved induction motor performance.PloS one · 2025Article
- 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
- Induction Motor Fault Diagnosis Using Support Vector Machine, Neural Networks, and Boosting Methods.Sensors (Basel, Switzerland) · 2023Article
- LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS).Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The induction motor plays a vital role in industrial drive systems due to its robustness and easy maintenance but at the same time, it suffers electrical faults, mainly rotor faults such as broken rotor bars. Early shortcoming identification is needed to lessen support expenses and hinder high costs by using failure detection frameworks that give features extraction and pattern grouping of the issue to distinguish the failure in an induction motor using classification models. In this paper, the open-source dataset of the rotor with the broken bars in a three-phase induction motor available on the IEEE data port is used for fault classification. The study aims at fault identification under various loading conditions on the rotor of an induction motor by performing time, frequency, and time-frequency domain feature extraction. The extracted features are provided to the models to classify between the healthy and faulty rotors. The extracted features from the time and frequency domain give an accuracy of up to 87.52% and 88.58%, respectively, using the Random-Forest (RF) model. Whereas, in time-frequency, the Short Time Fourier Transform (STFT) based spectrograms provide reasonably high accuracy, around 97.67%, using a Convolutional Neural Network (CNN) based fine-tuned transfer learning framework for diagnosing induction motor rotor bar severity under various loading conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.