What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mechanical engineering
- Statistics
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.
His most cited work include:
- Wavelets for fault diagnosis of rotary machines: A review with applications (738 citations)
- Artificial intelligence for fault diagnosis of rotating machinery: A review (493 citations)
- A sparse auto-encoder-based deep neural network approach for induction motor faults classification (329 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Algorithm (26.17%)
- Vibration (22.22%)
- Fault (20.49%)
What were the highlights of his more recent work (between 2019-2021)?
- Algorithm (26.17%)
- Fault (20.49%)
- Vibration (22.22%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Knowledge Transfer for Rotary Machine Fault Diagnosis (34 citations)
- Cyclostationary modeling for local fault diagnosis of planetary gear vibration signals (14 citations)
- Few-shot Transfer Learning for Intelligent Fault Diagnosis of Machine (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mechanical engineering
- Statistics
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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