Compound Fault Diagnosis of Stator Interturn Short Circuit and Air Gap Eccentricity Based on Random Forest and XGBoost

Tian, Rui; Chen, Fuyang; Dong, Shujun

doi:10.1155/2021/2149048

Cited by 12 publications

(7 citation statements)

References 32 publications

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“…Extreme gradient boosting decision tree (XGBoost) [27,29] is a large-scale parallel lifting tree algorithm. Similar to GBDT, it also adopts the boosting method, but its objective function is different from GBDT.…”

Section: Extreme Gradient Boosting Decision Treementioning

confidence: 99%

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Real-Time Underwater Acoustic Homing Weapon Target Recognition Based on a Stacking Technique of Ensemble Learning

Deng,

Yang,

Liu

et al. 2023

JMSE

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Underwater acoustic homing weapons (UAHWs) are formidable underwater weapons with the capability to detect, identify, and rapidly engage targets. Swift and precise target identification is crucial for the successful engagement of targets via UAHWs. This study presents a real-time target recognition method for UAHWs based on stacking ensemble technology. UAHWs emit active broadband detection signals that manifest distinct reflection characteristics on the target. Consequently, we have extracted energy and spatial distribution features from the target’s broadband correlation detection output. To address the problem of imbalanced original sea trial data, we employed the SMOTE algorithm to generate a relatively balanced dataset. Then, we established a stacking ensemble model and performed training and testing on both the original dataset and relatively balanced dataset separately. In conclusion, we deployed the stacking ensemble model on an embedded system. The proposed method was validated using real underwater acoustic homing weapon sea trial data. The experiment utilized 5-fold cross-validation. The results indicate that the method presented in this study achieved an average accuracy of 93.3%, surpassing that of individual classifiers. The model’s single-cycle inference time was 15 ms, meeting real-time requirements.

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Section: Extreme Gradient Boosting Decision Treementioning

confidence: 99%

“…Decision tree model.2.2.5. Random ForestRandom forest (RF) is an ensemble classifier based on DTs[27]. It uses the bootstrap resampling technique repeatedly to extract n different samples from the original dataset to create a new training sample set to train the decision trees and then generates n decision tree classifiers.…”

mentioning

confidence: 99%

Real-Time Underwater Acoustic Homing Weapon Target Recognition Based on a Stacking Technique of Ensemble Learning

Deng,

Yang,

Liu

et al. 2023

JMSE

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show abstract

“…This is also the necessity of data extraction in the duct work cycle and BP neural network simulation. There are also other deep learning methods that play a guiding role in the fault diagnosis in high-speed trains [17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Steps In Fault Diagnosis For Air Brake Pipesmentioning

confidence: 99%

Data-Driven Model Space Method for Fault Diagnosis of High-Speed Train Air Brake Pipes

Wang

Song

et al. 2023

Applied Sciences

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A data-driven fault diagnosis method is proposed in this study to address the challenge of handling a large volume of pressure data in the air brake pipe of high-speed trains. The suggested method utilizes a BP (back propagation) neural network to transform the time series pressure data into model elements in the model space, ensuring simplicity and stability. Various fitting functions, including Fourier basis, Gaussian basis, polynomial basis, sine basis, and others, are employed to accurately fit the pressure curve of the air brake pipe. The fault diagnosis process involves two steps: classifying the fault based on an optimal approximation equation and diagnosing it by analyzing the topological relationship of the model elements in the model space. The proposed method achieves an average fault diagnosis accuracy of 89.8%, with high accuracy rates for different fault states: 98% for normal state, 88% for blockage state, 84% for leakage state, and 96% for compressor fault state. Compared to the hidden Markov model method, the proposed method improves the average diagnostic accuracy by 2% for known working conditions and 4.87% for all working conditions, demonstrating its effectiveness and reliability. The fault diagnosis of the air brake tube in high-speed trains is of great significance, which aims to realize accurate fault diagnosis and prediction through sensor data monitoring and signal processing technology, so as to ensure the safe operation of high-speed trains. These studies provide an important theoretical and practical basis for the improvement and application of fault diagnosis methods.

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“…Shallow machine learning and deep learning methods are also applied to motor stator ITSC fault diagnosis [32][33][34][35]. The ITSC fault diagnosis method based on shallow machine learning is generally divided into three stages: data preparation, feature extraction, and model training.…”

Section: Introductionmentioning

confidence: 99%

Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on apFFT Time-Shift Phase Difference Spectrum Correction and SVM

Liu

et al. 2023

Energies

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EMU (electric multiple unit) traction motors are powered by converters whose output voltage increases the voltage stress borne by the insulation system, making the ITSC (inter-turn short-circuit) fault more prominent. An index based on short-circuit thermal power is proposed in the article to evaluate the non-metallic ITSC faults extent. The apFFT (all-phase FFT) time-shift phase difference correction with double Hanning windows is used to calculate fault features to train the SVM (support vector machine) fault diagnosis model whose hyper-parameters C and g are optimized using grid search methods. The experimental verification was carried out on the EMU electric traction simulation experimental platform. According to the fault extent index proposed in this article, the experimental samples were divided into three categories, normal, incipient and serious fault samples. The ITSC fault diagnosis accuracy was 100% on the training dataset and 93.33% on the test dataset. There was no misclassification between normal and serious ITSC fault samples.

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Compound Fault Diagnosis of Stator Interturn Short Circuit and Air Gap Eccentricity Based on Random Forest and XGBoost

Cited by 12 publications

References 32 publications

Real-Time Underwater Acoustic Homing Weapon Target Recognition Based on a Stacking Technique of Ensemble Learning

Real-Time Underwater Acoustic Homing Weapon Target Recognition Based on a Stacking Technique of Ensemble Learning

Data-Driven Model Space Method for Fault Diagnosis of High-Speed Train Air Brake Pipes

Stator ITSC Fault Diagnosis of EMU Asynchronous Traction Motor Based on apFFT Time-Shift Phase Difference Spectrum Correction and SVM

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