Support vector machine in machine condition monitoring and fault diagnosis. Widodo, A. & Yang, B. Mechanical Systems and Signal Processing, 21(6):2560–2574, August, 2007. ![Support vector machine in machine condition monitoring and fault diagnosis [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.
@article{widodo_support_2007,
title = {Support vector machine in machine condition monitoring and fault diagnosis},
volume = {21},
issn = {0888-3270},
url = {https://www.sciencedirect.com/science/article/pii/S0888327007000027},
doi = {10.1016/j.ymssp.2006.12.007},
abstract = {Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.},
language = {en},
number = {6},
urldate = {2021-09-30},
journal = {Mechanical Systems and Signal Processing},
author = {Widodo, Achmad and Yang, Bo-Suk},
month = aug,
year = {2007},
keywords = {Fault diagnosis, Machine condition monitoring, Support vector machine},
pages = {2560--2574},
}
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