Towards the deployment of machine learning solutions in network traffic classification: A systematic survey
F Pacheco, E Exposito, M Gineste… - … Surveys & Tutorials, 2018 - ieeexplore.ieee.org
Traffic analysis is a compound of strategies intended to find relationships, patterns,
anomalies, and misconfigurations, among others things, in Internet traffic. In particular, traffic …
anomalies, and misconfigurations, among others things, in Internet traffic. In particular, traffic …
A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines
In traditional intelligent fault diagnosis methods of machines, plenty of actual effort is taken
for the manual design of fault features, which makes these methods less automatic. Among …
for the manual design of fault features, which makes these methods less automatic. Among …
Feature engineering and artificial intelligence-supported approaches used for electric powertrain fault diagnosis: A review
Electric powertrain is constituted by electric machine transmission unit, inverter and battery
packs, etc., is a highly-integrated system. Its reliability and safety are not only related to …
packs, etc., is a highly-integrated system. Its reliability and safety are not only related to …
Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network
Traditional intelligent fault diagnosis methods for rolling bearings heavily depend on manual
feature extraction and feature selection. For this purpose, an intelligent deep learning …
feature extraction and feature selection. For this purpose, an intelligent deep learning …
Single and simultaneous fault diagnosis of gearbox via wavelet transform and improved deep residual network under imbalanced data
S Wang, J Tian, P Liang, X Xu, Z Yu, S Liu… - … Applications of Artificial …, 2024 - Elsevier
Playing a vital role in keeping gearbox working reliably and safely, smart fault diagnosis
(FD) technology has attracted much attention in recent years. However, in practical industrial …
(FD) technology has attracted much attention in recent years. However, in practical industrial …
Driver drowsiness detection based on steering wheel data applying adaptive neuro-fuzzy feature selection
This paper presents a novel feature selection method to design a non-invasive driver
drowsiness detection system based on steering wheel data. The proposed feature selector …
drowsiness detection system based on steering wheel data. The proposed feature selector …
Feature ranking for multi-fault diagnosis of rotating machinery by using random forest and KNN
Gearboxes and bearings play an important role in industries for motion and torque
transmission machines. Therefore, early diagnoses are sought to avoid unplanned …
transmission machines. Therefore, early diagnoses are sought to avoid unplanned …
Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery
Features extracted from real world applications increase dramatically, while machine
learning methods decrease their performance given the previous scenario, and feature …
learning methods decrease their performance given the previous scenario, and feature …
A comparison of fuzzy clustering algorithms for bearing fault diagnosis
Bearings are one of the most omnipresent and vulnerable components in rotary machinery
such as motors, generators, gearboxes, or wind turbines. The consequences of a bearing …
such as motors, generators, gearboxes, or wind turbines. The consequences of a bearing …
Enhanced sparse filtering with strong noise adaptability and its application on rotating machinery fault diagnosis
Z Zhang, S Li, J Wang, Y Xin, Z An, X Jiang - Neurocomputing, 2020 - Elsevier
Intelligent fault diagnosis is an effective method to guarantee the continuous and efficient
operation of rotating machinery. Compared with the experimental environment, noise is …
operation of rotating machinery. Compared with the experimental environment, noise is …