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Te Han
School of Management
h-index
3825
Citations
29
h-index
Calculated based on number of publications stored in Pure and citations from Scopus
2016
2025
Research activity per year
Overview
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Network
Research output
(87)
Similar Profiles
(1)
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Dive into the research topics where Te Han is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Weight
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Engineering
Fault Diagnosis
100%
Rolling Bearings
30%
Gearbox
29%
Feature Extraction
24%
Deep Learning
24%
Lithium-Ion Batteries
24%
Rotors
23%
Rotating Machinery
21%
Impact Fault
20%
Rotor System
19%
Empirical-Mode Decomposition
19%
Transfer Learning
15%
Variational Mode Decomposition
15%
Convolutional Neural Network
15%
Battery (Electrochemical Energy Engineering)
13%
Transmissions
13%
Performance Degradation
12%
Experimental Result
12%
Photovoltaic System
11%
Health Monitoring
11%
Dynamic Models
10%
Long Short-Term Memory
10%
Wind Turbine
10%
State of Health
10%
Battery Energy Storage
10%
Battery Capacity
9%
Finite Element Method
8%
Similarities
8%
Photovoltaics
7%
Target Data
7%
Multiscale
7%
Metrics
7%
Frequency Domain
7%
Tasks
7%
Simulation Experiment
6%
Regularization
6%
Spatial Feature
6%
Random Forest
6%
Demodulation
6%
Gaussian Mixture Model
6%
Singular Value
6%
Frequency Component
5%
Dictionary Learning
5%
Learning Approach
5%
Pump Unit
5%
Impact Dynamics
5%
Signal Voltage
5%
Breakage
5%
Measuring Point
5%
Load Condition
5%
Computer Science
Fault Diagnosis
89%
Deep Learning
38%
Intelligent Fault Diagnosis
36%
Machinery Fault Diagnosis
30%
fault diagnose method
29%
Domain Adaptation
25%
Feature Extraction
20%
Convolutional Neural Network
19%
Training Data
18%
Experimental Result
18%
Learning Approach
15%
Imbalanced Data
15%
Anomaly Detection
15%
Deep Learning Model
13%
Data Distribution
13%
Learning Framework
12%
Random Decision Forest
11%
Support Vector Machine
11%
Transfer Learning
11%
multi sensor
10%
Regularization
10%
Feature Fusion
10%
Representation Learning
10%
Clustering Algorithm
10%
Adversarial Machine Learning
10%
Supervised Learning
9%
Operating Condition
9%
Attention (Machine Learning)
9%
Condition Monitoring
8%
Case Study
7%
fault identification
6%
Transmission System
6%
Discriminative Feature
6%
Neural Network
5%
Feature Selection
5%
Dictionary Learning
5%
Deep Transfer Learning
5%
Fault Detection
5%
Feature Space
5%
Constant Variable
5%
Graph Neural Network
5%
Kernel Ridge Regression
5%
Industrial System
5%
Sparse Representation
5%
Critical Component
5%
Industrial Robot
5%
Health Condition
5%
Subject Domain
5%
Computer Interface
5%
Limited Sample Size
5%
