Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines-Reference-Cited by-同舟云学术

Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines

Published:2006-10 Issue:7 Volume:20 Page:1523-1536
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Rojas Alfonso,Nandi Asoke K.

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference17 articles.

1. Support vector machines for detection and characterization of rolling element bearing faults;Jack;Proceedings of Institution of Mechanical Engineers, Part C,2002

2. Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms;Jack;Mechanical Systems and Signal Processing,2002

3. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features;Samanta;Mechanical Systems and Signal Processing,2003

4. Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where ‘unknown’ faults may occur;Li;Pattern Recognition Letters,2002

5. Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines;Yang;Mechanical Systems and Signal Processing,2005

Cited by 63 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application of Industry 4.0 and Meta Learning for Bearing Fault Classification;2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD);2022-05-04

2. Predictive Monitoring of Incipient Faults in Rotating Machinery: A Systematic Review from Data Acquisition to Artificial Intelligence;Archives of Computational Methods in Engineering;2022-03-05

3. Fault Diagnosis of Bearings Using Recurrences and Artificial Intelligence Techniques;Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems;2022-03-01

4. Time domain vibration analysis techniques for condition monitoring of rolling element bearing: A review;Materials Today: Proceedings;2022

5. Ball Bearing Monitoring Using Decision-Tree and Adaptive Neuro-Fuzzy Inference System;Artificial Intelligence and Its Applications;2022