The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
Support Vector Data Description
Machine Learning
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Intrusion Detection by Ellipsoid Boundary
Journal of Network and Systems Management
New multi-class classification method based on the SVDD model
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
| Hi-index | 12.05 |
Abnormalities should be detected reliably so that corrective actions can be taken to maintain a high quality level of products. Fault or outlier detection provides early warning for a fault and identification of its assignable cause. The availability of large real-time datasets has motivated the study of data-driven approaches to fault detection. Recently, many powerful kernel-based nonlinear learning techniques have been developed and shown to be very effective tools. As one of one-classification methods, SVDD is able to define a boundary around samples with a volume as small as possible. This paper proposes a data description-based detection method combined with orthogonal filtering for enhanced detection abilities. The orthogonal filtering as a preprocessing step is executed before SVDD modeling to remove unwanted variation of data. The performance of the proposed method was demonstrated using data of two test processes. The case study has shown that the proposed method produces reliable detection results. In addition, the use of SVDD combined with orthogonal filtering step outperformed other one-class classification-based detection methods.