Summation pollution of principal component analysis and an improved algorithm for location sensitive data

Li, Jingwei; Cai, Xiao‐Chuan

doi:10.1002/nla.2370

Cited by 6 publications

(1 citation statement)

References 25 publications

(19 reference statements)

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“…Principle component analysis (PCA) is often used for dimensionality reduction compression of data [25]. Its core idea is to map the n-dimensional features to k-dimensional space [26].…”

Section: Signal Feature Extraction and Optimizationmentioning

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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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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“…Principle component analysis (PCA) is often used for dimensionality reduction compression of data [25]. Its core idea is to map the n-dimensional features to k-dimensional space [26].…”

Section: Signal Feature Extraction and Optimizationmentioning

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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Machine learning and domain decomposition methods - a survey

Klawonn,

Lanser,

Weber

2024

Comput. Sci. Eng.

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Hybrid algorithms, which combine black-box machine learning methods with experience from traditional numerical methods and domain expertise from diverse application areas, are progressively gaining importance in scientific machine learning and various industrial domains, especially in computational science and engineering. In the present survey, several promising avenues of research will be examined which focus on the combination of machine learning (ML) and domain decomposition methods (DDMs). The aim of this survey is to provide an overview of existing work within this field and to structure it into domain decomposition for machine learning and machine learning-enhanced domain decomposition, including: domain decomposition for classical machine learning, domain decomposition to accelerate the training of physics-aware neural networks, machine learning to enhance the convergence properties or computational efficiency of DDMs, and machine learning as a discretization method in a DDM for the solution of PDEs. In each of these fields, we summarize existing work and key advances within a common framework and, finally, discuss ongoing challenges and opportunities for future research.

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