Intelligent approach for the industrialization of deep learning solutions applied to fault detection
Early fault detection, both in equipment and the products in process, is of paramount
importance in industrial processes to ensure the quality of the final product, avoid abnormal …
importance in industrial processes to ensure the quality of the final product, avoid abnormal …
[HTML][HTML] Vibration-based bearing fault diagnosis of high-speed trains: A literature review
Due to the advantages of comfort and safety, high-speed trains are gradually becoming the
mainstream public transport in China. Since the operating speed and mileage of high-speed …
mainstream public transport in China. Since the operating speed and mileage of high-speed …
Data-driven ToMFIR-based incipient fault detection and estimation for high-speed rail vehicle suspension systems
Fault detection and estimation issues of China railway high-speed (CRH) train suspension
systems in early stage are addressed in this article based on data-driven design of total …
systems in early stage are addressed in this article based on data-driven design of total …
Semi-supervised fault diagnosis of wheelset bearings in high-speed trains using autocorrelation and improved flow Gaussian mixture model
J Wu, Y Li, L Jia, G An, YF Li, J Antoni, G **n - Engineering Applications of …, 2024 - Elsevier
Accurately diagnosing the faults of wheelset bearings in high-speed trains is critical for
ensuring the safety of train operation. In recent years, deep learning methods have made …
ensuring the safety of train operation. In recent years, deep learning methods have made …
Surface stress monitoring of laser shock peening using AE time-scale texture image and multi-scale blueprint separable convolutional networks with attention …
The quality monitoring of the laser shock peening (LSP) process is of great significance for
improving the intelligence of precision manufacturing. At present, the monitoring method …
improving the intelligence of precision manufacturing. At present, the monitoring method …
A new deep tensor autoencoder network for unsupervised health indicator construction and degradation state evaluation of metro wheel
For metro vehicles, wheel performance degradation is inevitable due to the continuous wear
of tread and rim. It is of significant value to apply machine learning techniques to evaluate …
of tread and rim. It is of significant value to apply machine learning techniques to evaluate …
SWDAE: A New Degradation State Evaluation Method for Metro Wheels With Interpretable Health Indicator Construction Based on Unsupervised Deep Learning
Machine learning has shown advantages in assessing wheel degradation in metro vehicles
for fault prognostic and health management (PHM). However, practical implementation faces …
for fault prognostic and health management (PHM). However, practical implementation faces …
Intelligent fault diagnosis and health stage division of bearing based on tensor clustering and feature space denoising
High-dimensional and unlabeled data collected from multi-sensor is a common scenario in
practical production. The fault diagnosis and health stage (HS) division of bearing under …
practical production. The fault diagnosis and health stage (HS) division of bearing under …
A lightweight dual-compression fault diagnosis framework for high-speed train bogie bearing
Y Li, S Wang, J **e, T Wang, J Yang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The vibration monitoring data of high-speed train (HST) bogie bearings exhibit high
redundancy and limited effective fault information, impacting diagnostic accuracy and speed …
redundancy and limited effective fault information, impacting diagnostic accuracy and speed …
A novel Move-Split-Merge based Fuzzy C-Means algorithm for clustering time series
W Ba, Z Gu - Evolving Systems, 2024 - Springer
When faced with noisy time series data, significant challenges are encountered by clustering
algorithms, including noise interference, temporal distortions, and irregular data patterns. In …
algorithms, including noise interference, temporal distortions, and irregular data patterns. In …