What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Artificial intelligence, Pattern recognition, Feature extraction, Signal processing and Data mining.
His work deals with themes such as Vibration and Kurtosis, which intersect with Artificial intelligence.
He has included themes like Transfer of learning and Probability distribution in his Pattern recognition study.
The various areas that Yaguo Lei examines in his Feature extraction study include Control engineering, Stochastic resonance and Control theory.
His research in Signal processing intersects with topics in Hilbert–Huang transform, Mode, Electronic engineering and Nonlinear system.
His Data mining study integrates concerns from other disciplines, such as Recurrent neural network, Deep learning and Bearing.
His most cited work include:
- A review on empirical mode decomposition in fault diagnosis of rotating machinery (947 citations)
- Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data (759 citations)
- An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data (494 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Vibration, Artificial intelligence, Feature extraction, Pattern recognition and Data mining.
His biological study spans a wide range of topics, including Algorithm, Bearing, Control theory and Fault detection and isolation.
The concepts of his Artificial intelligence study are interwoven with issues in Prognostics and Machine learning.
His Pattern recognition research incorporates elements of Hilbert–Huang transform and Sensitivity.
His Hilbert–Huang transform research focuses on Signal processing and how it connects with Mechanical equipment and Working environment.
His Data mining research also works with subjects such as
- Artificial neural network that intertwine with fields like Raw data,
- Transfer of learning and related Bridge.
He most often published in these fields:
- Vibration (35.71%)
- Artificial intelligence (31.25%)
- Feature extraction (23.21%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (31.25%)
- Machine learning (9.82%)
- Deep learning (8.93%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Machine learning, Deep learning, Algorithm and Feature extraction are his primary areas of study.
His work on Transfer of learning as part of general Machine learning study is frequently connected to Life testing, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Deep learning research is multidisciplinary, incorporating elements of Prognostics and Feature learning.
His study in the field of Residual also crosses realms of Polynomial kernel.
His study with Feature extraction involves better knowledge in Pattern recognition.
His research integrates issues of Rolling-element bearing and Big data in his study of Pattern recognition.
Between 2019 and 2021, his most popular works were:
- Applications of machine learning to machine fault diagnosis: A review and roadmap (168 citations)
- A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings (116 citations)
- Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Artificial intelligence, Machine learning, Deep learning, Prognostics and Kernel.
Yaguo Lei interconnects Rolling-element bearing and Big data in the investigation of issues within Artificial intelligence.
In general Machine learning, his work in Feature learning and Convolutional neural network is often linked to Space linking many areas of study.
The Prognostics study combines topics in areas such as Vibration and Support vector machine, Relevance vector machine.
His Kernel research integrates issues from Time complexity and Kernel.
The study incorporates disciplines such as Algorithm and Moment in addition to Transfer of learning.
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