Proceedings ArticleDOI
Forecasting Conditions of Reactor Coolant Pump Based on Support Vector Machine
Meng Qinghu,Meng Qingfeng,Feng Wuwei +2 more
- Vol. 5, pp 293-297
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TLDR
A forecast model which combines LSSVM (Least Squares Support Vector Machine) and Time Series model is constructed and the impact to forecast accuracy which caused by embedding dimension M, kernel function σ and regularization parameter γ is studied.Abstract:
In the traditional fault diagnosis technology, classical life and reliability tests require sufficient sample size when diagnose the faults and forecast the future states. However, there is even less sample size for machinery products, especially for major equipment. The Support Vector Machine based on Statistical Learning Theory can solve this problem. In this paper, a forecast model for reactor coolant pump which combines LSSVM (Least Squares Support Vector Machine) and Time Series model is constructed. We studied the impact to forecast accuracy which caused by embedding dimension M, kernel function σ and regularization parameter γ. Meanwhile, the performance of LSSVM is verified by simulation data and field data. Then LSSVM is used to predict vibration signals of reactor coolant pump. As it is certified that the forecast data could match the actual data preferably and has achieved good results in forecasting field data.read more
Citations
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Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
A survey of the state of condition-based maintenance (CBM) in the nuclear power industry
R.M. Ayo-Imoru,A.C. Cilliers +1 more
TL;DR: In this paper, the authors present a survey on the state of condition-based maintenance in the nuclear industry, which is achieved by systematically looking at the major phases of CBM, which are monitoring, diagnostics and prognostics.
Proceedings ArticleDOI
Anticipatory monitoring and control of complex energy systems using a fuzzy based fusion of support vector regressors
TL;DR: An intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested and it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.
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Optimal assembly of support vector regressors with application to system monitoring
TL;DR: The field of machine learning appears to offer the necessary tools for developing automated instrumentation systems running risks of low frequency but high consequence in power plants.
Journal ArticleDOI
Product Quality Modelling Based on Incremental Support Vector Machine
TL;DR: The proposed modified incremental support vector machine (MISVM) can not only eliminate the unimportant samples such as noise samples, but also can preserve the important samples and improve the prediction accuracy and the training speed effectively.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Proceedings ArticleDOI
The Mathematics of Learning: Dealing With Data
Tomaso Poggio,Steve Smale +1 more
TL;DR: The mathematical foundations of learning theory are outlined and a key algorithm of it is described, which is key to developing systems tailored to a broad range of data analysis and information extraction tasks.
Journal ArticleDOI
Neural networks in process fault diagnosis
TL;DR: A multilayer perceptron network with a hyperbolic tangent as the nonlinear element seems best suited for the task of fault diagnosis in a realistic heat exchanger-continuous stirred tank reactor system.
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