Advanced Fault Detection and Severity Analysis of Broken Rotor Bars in Induction Motors: Comparative Classification and Feature Study Using Dimensionality Reduction Techniques
Abstract
1. Introduction
1.1. Related Works
1.2. Summary of Open Problems and Paper Objective
2. Materials and Methods
2.1. Methodology
- (a)
- Problem Definition (BRBs): One of the most common IM faults is the broken rotor bar fault. If not detected in a timely manner, this fault can cause significant financial losses and production delays for industries. According to statistics presented in [32], BRB faults contribute to 8–9% of IM faults. Therefore, early identification and precise determination of this fault is crucial to prevent such setbacks.
- (b)
- Fault generation: Experimentation on BRBs was performed with a grid-connected three-phase faulty IM. To induce a fault on the IM, holes were drilled into the rotor bar to determine the fault severity. The motor was then run under the specified configuration, and the corresponding current signatures were recorded. Severity levels created included 1.5 BRBs, 2 BRBs, 2.5 BRBs, and 3 BRBs.
- (c)
- Data acquisition: Three current sensors were used to display the three-phase current signatures on an oscilloscope and then stored on a hard drive. The stator-current signal was acquired at a sampling frequency of 250 Hz, selected to ensure an efficient balance between data volume and computational demands for the proposed machine learning model training. Multiple trials were conducted for each test scenario, with 10 s of data collected per trial, yielding a robust dataset for analysis. This sampling rate was found to be sufficient for capturing the fundamental frequency and primary CFFs associated with BRB faults. While a higher sampling frequency may have improved the resolution of fault indicators, especially for higher-order harmonics, the current sampling rate proved to be effective for the intended quick-detection application and avoided computational delays during processing of the signals.
- (d)
- Data Handling: Stator current signals were processed through several data handling steps to ensure high-quality input for feature extraction and classification. First, the signals were segmented into fixed-length frames, allowing for efficient feature extraction from discrete intervals. Normalization was then applied to each frame to maintain consistent amplitude across samples, which enhanced model accuracy by reducing variance due to signal amplitude changes. This preprocessing pipeline effectively prepared the data for dimensionality reduction and classification, ensuring that fault-specific characteristics were accurately captured and used in the diagnostic model.
- (e)
- Exploratory analysis of data: From the raw data, Parks and extended Parks quantity were calculated, followed by the calculation of the fifteen statistical time-domain features. The data were analyzed using frequency analysis by performing the FFT technique to view the CFFs. The data were further analyzed using exploratory analysis tools like PCA, CCA, and ICA.
- (f)
- Model development and training: After going through the exploratory analysis of data, neural and non-neural-based models were designed using MATLAB® software (r2021b). Feature inputs included fifteen time-domain statistical features and transformed PCA, CCA, and ICA features. Standard procedures for partitioning the dataset into training, validation, and test sets were adopted.
- (g)
- Classification of test dataset: Using the test-sets (full features and reduced feature-sets), neural network models, and a few non-neural techniques like decision trees, SVM, and ensemble bagged trees, the BRB faults were analyzed. Furthermore, the accuracies were compared for performance evaluation.
2.2. Experimental Test Rig
3. Results and Discussion
3.1. Frequency Analysis: Fast Fourier Transform
3.1.1. Faulty IM: Different Severity Levels at Different Load Conditions
3.1.2. Park’s and Extended Park Quantity–Frequency Spectrum
3.2. Feature Extraction and Exploratory Analysis
3.3. Dataset Partitioning
- Training dataset (3500 samples): 70%;
- Validation dataset (750 samples): 15%;
- Test dataset (750 samples): 15%.
One-Hot Encoding
3.4. Classification Using Neural and Non-Neural Based Techniques
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Motor Type | Squirrel Cage |
---|---|
Power | |
Speed | |
Frequency | |
Rated Voltage | |
Rated Current | |
Number of pair poles | 2 |
Number of rotor slots | 28 |
Number of stator slots | 36 |
Class Type | B |
Fault Class | Fault Severity | Broken Rotor Bars |
---|---|---|
1 | Healthy | 0 |
2 | 2 BRBs (50% severity) | 1.5 |
3 | 2 BRBs (100% severity) | 2 |
4 | 3 BRBs (50% severity) | 2.5 |
5 | 3 BRBs (100% severity) | 3 |
Classifier | Normalized 15 Features | PCA | CCA | ICA | Comments |
---|---|---|---|---|---|
Shallow Dense NN | 93.70 | 96.70 | 90.80 | 82.50 | Number of neurons = 9, Architecture: * IN|FC|5OUT, Activation-SoftMax layer |
LSTM NN | 91.60 | 96.67 | 89.87 | 83.73 | Number of neurons = 30, Architecture: * IN|FC|5OUT, Activation-SoftMax layer |
Fine Tree | 86.1 | 76.0 | 70.3 | 67.5 | Max. Number of Splits = 100, Split Criterion: Gini’s Diversity index |
Medium Tree | 86.9 | 77.3 | 66.1 | 72.0 | Max. Number of Splits = 20, Split Criterion: Gini’s Diversity index |
Course Tree | 84.1 | 68.0 | 68.3 | 70.3 | Max. Number of Splits = 4, Split Criterion: Gini’s Diversity index |
Quadratic Discriminant | 94.9 | 94.4 | 94.9 | 70.3 | Full Covariance Structure |
Quadratic SVM | 91.2 | 92.0 | 94.1 | 68.5 | Kernel Function: Quadratic |
Cubic SVM | 90.5 | 89.3 | 80.7 | 74.7 | Kernel Function: Cubic |
Fine Gaussian SVM | 83.9 | 50.5 | 75.9 | 76.8 | Kernel Function: Gaussian |
Class | Sensitivity (Recall) | Specificity | Precision | F1 Score |
---|---|---|---|---|
1 | 1 | 1 | 1 | 1 |
2 | 1 | 1 | 1 | 1 |
3 | 1 | 1 | 1 | 1 |
4 | 0.919 | 0.974 | 0.919 | 0.919 |
5 | 0.906 | 0.981 | 0.906 | 0.906 |
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Kumar, R.R.; Waisale, L.O.; Tamata, J.L.; Tortella, A.; H. Kia, S.; Andriollo, M. Advanced Fault Detection and Severity Analysis of Broken Rotor Bars in Induction Motors: Comparative Classification and Feature Study Using Dimensionality Reduction Techniques. Machines 2024, 12, 890. https://doi.org/10.3390/machines12120890
Kumar RR, Waisale LO, Tamata JL, Tortella A, H. Kia S, Andriollo M. Advanced Fault Detection and Severity Analysis of Broken Rotor Bars in Induction Motors: Comparative Classification and Feature Study Using Dimensionality Reduction Techniques. Machines. 2024; 12(12):890. https://doi.org/10.3390/machines12120890
Chicago/Turabian StyleKumar, Rahul R., Litili O. Waisale, Jiuta L. Tamata, Andrea Tortella, Shahin H. Kia, and Mauro Andriollo. 2024. "Advanced Fault Detection and Severity Analysis of Broken Rotor Bars in Induction Motors: Comparative Classification and Feature Study Using Dimensionality Reduction Techniques" Machines 12, no. 12: 890. https://doi.org/10.3390/machines12120890
APA StyleKumar, R. R., Waisale, L. O., Tamata, J. L., Tortella, A., H. Kia, S., & Andriollo, M. (2024). Advanced Fault Detection and Severity Analysis of Broken Rotor Bars in Induction Motors: Comparative Classification and Feature Study Using Dimensionality Reduction Techniques. Machines, 12(12), 890. https://doi.org/10.3390/machines12120890