Online support vector regression approach for the monitoring of motor shaft misalignment and feedwater flow rate

Olufemi A. Omitaomu; Myong K. Jeong; Adedeji B. Badiru; J. Wesley Hines

doi:10.1109/TSMCC.2007.900648

Online support vector regression approach for the monitoring of motor shaft misalignment and feedwater flow rate

Olufemi A. Omitaomu, Myong K. Jeong, Adedeji B. Badiru, J. Wesley Hines

Computational Urban Sciences

Research output: Contribution to journal › Article › peer-review

28 Scopus citations

Abstract

Timely and accurate information about incipient faults in online machines will greatly enhance the development of optimal maintenance procedures. The application of support vector regression to machine health monitoring was recently investigated; however, such implementation is based on batch processing of the available data. Therefore, the addition of new sample to the already existing dataset requires that the technique should retrain from scratch. This disadvantage makes the technique unsuitable for online systems that will give real-time information to field engineers so that corrective actions could be taken before there is any damage to the system. This paper presents an application of accurate online support vector regression (AOSVR) approach that efficiently updates a trained predictor whenever a new sample is added to the training set using shaft misalignment and nuclear power plant feedwater flow rate data. The results show that the approach is effective for online machine condition monitoring where it is usually difficult to obtain sufficient training data prior to the installation of the online systems.

Original language	English
Pages (from-to)	962-970
Number of pages	9
Journal	IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume	37
Issue number	5
DOIs	https://doi.org/10.1109/TSMCC.2007.900648
State	Published - Sep 2007

Funding

Manuscript received February 15, 2005; revised June 1, 2006. This work was supported in part by the National Science Foundation under Grant CMMI-0644830. The views expressed in this paper are those of the authors and do not reflect the official policy or position of the Air Force Institute of Technology, the Oak Ridge National Laboratory, UT-Battelle, U.S. Air Force, Department of Defense, Department of Energy, or the U.S. Government. This paper was recommended by Associate Editor D. McFarlane.

Funders	Funder number
National Science Foundation	CMMI-0644830

Keywords

Data mining
Machine health monitoring
Nuclear power plant
Online condition monitoring systems

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10.1109/TSMCC.2007.900648

Cite this

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abstract = "Timely and accurate information about incipient faults in online machines will greatly enhance the development of optimal maintenance procedures. The application of support vector regression to machine health monitoring was recently investigated; however, such implementation is based on batch processing of the available data. Therefore, the addition of new sample to the already existing dataset requires that the technique should retrain from scratch. This disadvantage makes the technique unsuitable for online systems that will give real-time information to field engineers so that corrective actions could be taken before there is any damage to the system. This paper presents an application of accurate online support vector regression (AOSVR) approach that efficiently updates a trained predictor whenever a new sample is added to the training set using shaft misalignment and nuclear power plant feedwater flow rate data. The results show that the approach is effective for online machine condition monitoring where it is usually difficult to obtain sufficient training data prior to the installation of the online systems.",

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Online support vector regression approach for the monitoring of motor shaft misalignment and feedwater flow rate. / Omitaomu, Olufemi A.; Jeong, Myong K.; Badiru, Adedeji B. et al.
In: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, Vol. 37, No. 5, 09.2007, p. 962-970.

Research output: Contribution to journal › Article › peer-review

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AU - Omitaomu, Olufemi A.

AU - Jeong, Myong K.

AU - Badiru, Adedeji B.

AU - Hines, J. Wesley

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AB - Timely and accurate information about incipient faults in online machines will greatly enhance the development of optimal maintenance procedures. The application of support vector regression to machine health monitoring was recently investigated; however, such implementation is based on batch processing of the available data. Therefore, the addition of new sample to the already existing dataset requires that the technique should retrain from scratch. This disadvantage makes the technique unsuitable for online systems that will give real-time information to field engineers so that corrective actions could be taken before there is any damage to the system. This paper presents an application of accurate online support vector regression (AOSVR) approach that efficiently updates a trained predictor whenever a new sample is added to the training set using shaft misalignment and nuclear power plant feedwater flow rate data. The results show that the approach is effective for online machine condition monitoring where it is usually difficult to obtain sufficient training data prior to the installation of the online systems.

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Online support vector regression approach for the monitoring of motor shaft misalignment and feedwater flow rate

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