What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Mechanical engineering
His main research concerns Fault, Vibration, Acoustics, Energy harvesting and Energy.
His Fault research includes elements of Artificial neural network, Feature extraction, Artificial intelligence, Noise and Signal processing.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Raw data and Data mining.
His Vibration research incorporates themes from Hidden Markov model, Probability density function, Prediction algorithms and Mutual information.
His work in Energy harvesting addresses issues such as Nonlinear system, which are connected to fields such as Piezoelectricity.
The concepts of his Energy study are interwoven with issues in Restoring force and Bistability.
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 primary scientific interests are in Vibration, Fault, Control theory, Acoustics and Algorithm.
He combines subjects such as Energy harvesting, Energy, Harmonic, Electronic engineering and Kurtosis with his study of Vibration.
His work deals with themes such as Noise, Condition monitoring, Feature extraction, Artificial intelligence and Bearing, which intersect with Fault.
His work in Noise covers topics such as Signal processing which are related to areas like Control engineering.
His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition.
The Control theory study combines topics in areas such as Stochastic resonance and Fault detection and isolation.
He most often published in these fields:
- Vibration (31.48%)
- Fault (32.10%)
- Control theory (28.40%)
What were the highlights of his more recent work (between 2019-2021)?
- Lamb waves (14.81%)
- Fault (32.10%)
- Algorithm (17.28%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Lamb waves, Fault, Algorithm, Acoustics and Materials science.
His Fault study integrates concerns from other disciplines, such as Singular value decomposition, Machine learning, Feature, Artificial intelligence and Bearing.
The study incorporates disciplines such as Field, Planetary gearbox and Pattern recognition in addition to Artificial intelligence.
His Algorithm research includes themes of Encoder, Signal, Robustness and Harmonic.
He has included themes like Composite laminates and Structural health monitoring in his Acoustics study.
His studies in Deconvolution integrate themes in fields like Vibration and Adaptive filter.
Between 2019 and 2021, his most popular works were:
- A comprehensive review on convolutional neural network in machine fault diagnosis (22 citations)
- Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal (14 citations)
- Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Mechanical engineering
His primary areas of investigation include Encoder, Algorithm, Fault, Signal and Convolutional neural network.
His Encoder research incorporates elements of Deconvolution, Wind power, Adaptive filter and Turbine.
His biological study spans a wide range of topics, including Feature extraction and Background noise.
The study incorporates disciplines such as Decomposition method, Noise, Mode and Signal processing in addition to Fault.
Jing Lin has included themes like Filter, Iterative method, Noise measurement, Harmonic and Optimization problem in his Signal study.
His Convolutional neural network study combines topics in areas such as Classifier and Field.
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