Bearing fault diagnostics using EEMD processing and convolutional neural network methods
IIE Amarouayache, MN Saadi, N Guersi… - … International Journal of …, 2020 - Springer
The development of an intelligent fault diagnosis system to identify automatically and
accurately micro-faults affecting motors continues to be a challenge for industrial rotary …
accurately micro-faults affecting motors continues to be a challenge for industrial rotary …
A novel hybrid feature extraction approach of marine vessel signal via improved empirical mode decomposition and measuring complexity
The feature extraction of marine vessel-radiated noise (MVRN) under the complex ocean
background is explored. To this end, a hybrid approach is presented based on the analysis …
background is explored. To this end, a hybrid approach is presented based on the analysis …
Classification of atrial fibrillation and normal sinus rhythm based on convolutional neural network
ML Huang, YS Wu - Biomedical engineering letters, 2020 - Springer
Electrocardiogram (ECG) technology plays a vital role in detecting arrhythmia. Numerous
achievements have been marked in ECG-related research. Most methods first pre-process …
achievements have been marked in ECG-related research. Most methods first pre-process …
An intelligent hybrid scheme for identification of faults in industrial ball screw linear motion systems
Reliability of high precision linear motion system is one of the main concerns in industrial
and military systems. The performance and repeatability of these systems are influenced by …
and military systems. The performance and repeatability of these systems are influenced by …
An efficient deep learning framework for P300 evoked related potential detection in EEG signal
Background Incorporating the time-frequency localization properties of Gabor transform
(GT), the complexity understandings of convolutional neural network (CNN), and histogram …
(GT), the complexity understandings of convolutional neural network (CNN), and histogram …
[HTML][HTML] Deep-learning-based approach to anomaly detection techniques for large acoustic data in machine operation
As the workforce shrinks, the demand for automatic, labor-saving, anomaly detection
technology that can perform maintenance on advanced equipment such as vehicles has …
technology that can perform maintenance on advanced equipment such as vehicles has …
[HTML][HTML] Combining the Taguchi Method and Convolutional Neural Networks for Arrhythmia Classification by Using ECG Images with Single Heartbeats
SF Li, ML Huang, YS Wu - Mathematics, 2023 - mdpi.com
In recent years, deep learning has been applied in numerous fields and has yielded
excellent results. Convolutional neural networks (CNNs) have been used to analyze …
excellent results. Convolutional neural networks (CNNs) have been used to analyze …
Tire Condition Classification Based on Tread Depth using Machine Learning
As a crucial component for vehicle performance and stability, automotive manufacturers
adopt specific tire inspection procedures to verify its quality and integrity. Tires stay in good …
adopt specific tire inspection procedures to verify its quality and integrity. Tires stay in good …
A lightweight CNN to identify cardiac arrhythmia using 2D ECG images
S El Omary, S Lahrache… - AI Applications for Disease …, 2022 - igi-global.com
Worldwide, cardiac arrhythmia disease has become one of the most frequent heart
problems, leading to death in most cases. In fact, cardiologists use the electrocardiogram …
problems, leading to death in most cases. In fact, cardiologists use the electrocardiogram …
Extending CNN classification capabilities using a novel feature to image transformation (FIT) algorithm
AS Salman, OS Salman, GE Katz - Intelligent Computing: Proceedings of …, 2020 - Springer
In this work, we developed a novel approach with two main components to process raw time-
series and other data forms as images. This includes a feature extraction component that …
series and other data forms as images. This includes a feature extraction component that …