His main research concerns Fault, Artificial intelligence, Algorithm, Wavelet and Pattern recognition. His Fault research incorporates elements of Feature, Control theory, Vibration and Stochastic resonance, Noise. In the field of Artificial intelligence, his study on Support vector machine, Feature extraction and Deep learning overlaps with subjects such as Field.
His studies deal with areas such as Convex optimization, Time–frequency analysis, Instantaneous phase and Signal processing as well as Algorithm. His studies in Wavelet integrate themes in fields like Mathematical analysis, Finite element method, Mathematical optimization, Applied mathematics and Stiffness. His Pattern recognition course of study focuses on Overfitting and Kernel and Time delay neural network.
Xuefeng Chen spends much of his time researching Algorithm, Vibration, Fault, Wavelet and Finite element method. His research integrates issues of Convex optimization, Time–frequency analysis and Signal processing in his study of Algorithm. His work carried out in the field of Vibration brings together such families of science as Structural engineering, Signal, Control theory and Rotor.
The study incorporates disciplines such as Feature, Artificial intelligence, Noise, Bearing and Pattern recognition in addition to Fault. The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Condition monitoring. His study in Wavelet is interdisciplinary in nature, drawing from both Mathematical analysis, Applied mathematics and Interval finite element.
Xuefeng Chen mostly deals with Algorithm, Fault, Vibration, Artificial intelligence and Feature extraction. His Algorithm study incorporates themes from Lasso, Noise and Feature. Within one scientific family, Xuefeng Chen focuses on topics pertaining to Data mining under Fault, and may sometimes address concerns connected to Task.
His Vibration research includes themes of Control theory, Condition monitoring, Signal, Stiffness and Blade. His work investigates the relationship between Artificial intelligence and topics such as Pattern recognition that intersect with problems in Autoencoder. While the research belongs to areas of Feature extraction, he spends his time largely on the problem of Training set, intersecting his research to questions surrounding Manifold.
His primary areas of investigation include Fault, Vibration, Algorithm, Feature extraction and Structural engineering. His Fault research incorporates themes from Representation, Fourier transform, Computer simulation, Transfer of learning and Bearing. His Vibration study incorporates themes from Signal, Control theory, Condition monitoring and Blade.
His Algorithm research includes elements of Kurtosis, Cluster analysis and Identification. The research on Pattern recognition and Artificial intelligence is part of his Feature extraction project. His work in the fields of Pattern recognition, such as Training set, overlaps with other areas such as Test data.
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Wavelets for fault diagnosis of rotary machines: A review with applications
Ruqiang Yan;Robert X. Gao;Xuefeng Chen.
Signal Processing (2014)
Artificial intelligence for fault diagnosis of rotating machinery: A review
Ruonan Liu;Boyuan Yang;Enrico Zio;Enrico Zio;Xuefeng Chen.
Mechanical Systems and Signal Processing (2018)
Artificial intelligence for fault diagnosis of rotating machinery: A review
Ruonan Liu;Boyuan Yang;Enrico Zio;Enrico Zio;Xuefeng Chen.
Mechanical Systems and Signal Processing (2018)
A sparse auto-encoder-based deep neural network approach for induction motor faults classification
Wenjun Sun;Siyu Shao;Rui Zhao;Ruqiang Yan;Ruqiang Yan.
Measurement (2016)
Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals
Jinglong Chen;Jun Pan;Zipeng Li;Yanyang Zi.
Renewable Energy (2016)
Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals
Jinglong Chen;Jun Pan;Zipeng Li;Yanyang Zi.
Renewable Energy (2016)
Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings
Zhiwen Liu;Hongrui Cao;Xuefeng Chen;Zhengjia He.
Neurocomputing (2013)
Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings
Zhiwen Liu;Hongrui Cao;Xuefeng Chen;Zhengjia He.
Neurocomputing (2013)
New clustering algorithm-based fault diagnosis using compensation distance evaluation technique
Yaguo Lei;Zhengjia He;Yanyang Zi;Xuefeng Chen.
Mechanical Systems and Signal Processing (2008)
New clustering algorithm-based fault diagnosis using compensation distance evaluation technique
Yaguo Lei;Zhengjia He;Yanyang Zi;Xuefeng Chen.
Mechanical Systems and Signal Processing (2008)
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