A Review of Fault Diagnosis for Traction Induction Motor

Tian, Yu; Guo, Dingfei; Zhang, Kunting; Jia, Liguo; Qiao, Hong; Tang, Haichuan

doi:10.23919/chicc.2018.8484044

Cited by 15 publications

(9 citation statements)

References 40 publications

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“…In formula (8), the convolution coefficient of the first row of the vector in the H matrix is extremely required. According to the expression of the H matrix, it is only determined by the highest coefficient of the least square polynomial and the size of the filter window, which is independent of the original data.…”

Section: Signal Noise Reduction Based On Sg Filteringmentioning

confidence: 99%

“…For the analysis of the complex mechanical components' fault under various nonlinear responses, Keshtegar et al [7] proposed the Modified Response Surface basis Models for failure turbine blisk response which also appears to be multiphased, where it includes two regression processes for the purpose of regressing the input variables and calibration more precisely. Moreover, in the traction motor domain, the induction traction motor health diagnostics have been reviewed in [8]. Also, the new merged techniques such as deep learning [9] and transfer learning [10], where the detection knowledge could be learned from another domain, are quite useful when the data are deficient.…”

Section: Introductionmentioning

confidence: 99%

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

Tian

Chen

Dong

2021

Mathematical Problems in Engineering

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Taking the traction motor of CRH2 high-speed train as the research object, this paper proposes a diagnosis method based on random forest and XGBoost for the compound fault resulting from stator interturn short circuit and air gap eccentricity. First, the U-phase and V-phase currents are used as fault diagnosis signal and then the Savitzky–Golay filtering method is used for the noise deduction from the signal. Second, the wavelet packet decomposition is used to extract the composite fault features and then the high-dimensional features are optimized by the principal component analysis (PCA) method. Finally, the random forest and XGBoost are combined to detect composite faults. Using the experimental data of CRH2 semiphysical simulation platform, the diagnosis of different fault modes is completed, and the high diagnosis accuracy is achieved, which verifies the validity of this method.

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Section: Signal Noise Reduction Based On Sg Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tian

Chen

Dong

2021

Mathematical Problems in Engineering

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“…In addition, Nasiri et al [22] surveyed the state-of-the-art AI-based approaches for fracture mechanics and provided the accuracy comparisons achieved by different machine learning algorithms for mechanical fault detection. Tian et al [23] surveyed different modes of traction induction motor fault and their diagnosis algorithms including modelbased methods and AI-based methods. Khan et al [24] provided a comprehensive review of AI for system health management and emphasized the trend of DL-based methods with limitations and benefits.…”

Section: Brief Reviewmentioning

confidence: 99%

Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study

Zhao,

Li,

et al. 2020

Preprint

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With the development of artificial intelligence and deep learning (DL) techniques, rotating machinery intelligent diagnosis has gone through tremendous progress with verified success and the classification accuracies of many DL-based intelligent diagnosis algorithms are tending to 100%. However, different datasets, configurations, and hyper-parameters are often recommended to be used in performance verification for different types of models, and few open source codes are made public for evaluation and comparisons. Therefore, unfair comparisons and ineffective improvement may exist in rotating machinery intelligent diagnosis, which limits the advancement of this field. To address these issues, we perform an extensive evaluation of four kinds of models, including multi-layer perception (MLP), auto-encoder (AE), convolutional neural network (CNN), and recurrent neural network (RNN), with various datasets to provide a benchmark study within the same framework. In this paper, we first gather most of the publicly available datasets and give the complete benchmark study of DL-based intelligent algorithms under two data split strategies, five input formats, three normalization methods, and four augmentation methods. Second, we integrate the whole evaluation codes into a code library and release this code library to the public for better development of this field. Third, we use the specific-designed cases to point out the existing issues, including class imbalance, generalization ability, interpretability, few-shot learning, and model selection.By these works, we release a unified code framework for comparing and testing models fairly and quickly, emphasize the importance of open source codes, provide the baseline accuracy (a lower bound) to avoid useless improvement, and discuss potential future directions in this field. The code library is available at https://github.com/ZhaoZhibin/ DL-based-Intelligent-Diagnosis-Benchmark.

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“…Current methods based on the measurement of phase currents [26] and the study of changes in stator currents in different phases [27] also do not allow researchers to accurately determine the reasons for the manifestation of the asymmetry of the stator current system.…”

Section: Introductionmentioning

confidence: 99%

Method for Determining the Degree of Damage to the Stator Windings of an Induction Electric Motor with an Asymmetric Power System

Gerlici

Goolak²,

Gubarevych³

et al. 2022

Symmetry

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A method is proposed for determining the number of damaged stator windings in the presence of an asymmetric power supply system for an induction electric motor based on the Park vector hodograph. As a result of the experiments on the simulation model, it was found that with the symmetry of the system of supply voltages and stator windings, the hodograph of the Park vector describes a circle; in all other cases it is an ellipse. It has been established that the presence of asymmetry in the supply voltage system is indicated by the angle of inclination of the ellipse, and the indicator of the presence of the asymmetry of the stator windings is the angle of ellipticity. In order to identify the presence of asymmetry of the stator windings in the conditions of asymmetry of the supply voltage system, an algorithm for recalculating the ellipse parameters for the condition of the symmetry of the supply voltage system was proposed. Recalculation errors did not exceed 6%. It has been established that the dependence of the increment of the amplitudes of the phase and angles of the phase currents of the stator on the number of damaged turns of the stator winding is linear. Based on this fact, an algorithm for determining the number of damaged turns was proposed. The results of this work can be used to build systems for diagnosing the interturn short circuit of the stator of an induction electric motor built into the drive.

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A Review of Fault Diagnosis for Traction Induction Motor

Cited by 15 publications

References 40 publications

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

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

Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study

Method for Determining the Degree of Damage to the Stator Windings of an Induction Electric Motor with an Asymmetric Power System

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