Paper Info
Title
Online Support Vector Regression Approach for the Monitoring of Motor Shaft Misalignment and Feedwater Flow Rate
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.
Year
DOI
Venue
2007
10.1109/TSMCC.2007.900648
IEEE Transactions on Systems, Man, and Cybernetics, Part C
Keywords
Field
DocType
maintenance engineering,data mining,flow rate,couplings,support vector machines,real time,batch processing,batch process,support vector regression
Data processing,Computer science,System monitoring,Condition monitoring,Artificial intelligence,Support vector machine,Operations research,Real-time operating system,Optimal maintenance,Reliability engineering,Maintenance engineering,Machine learning,Boiler feedwater
Journal
Volume
Issue
ISSN
37
5
1094-6977
Citations 
PageRank 
References 
10
0.95
8
Authors
4
Name
Order
Citations
PageRank
Olufemi A. Omitaomu132117.51
Myong Kee Jeong2425.21
Adedeji B. Badiru35310.78
J. Wesley Hines4566.99