Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pump Failure Detection Using Support Vector Data Descriptions
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Support Vector Data Description
Machine Learning
An Approach to Novelty Detection Applied to the Classification of Image Regions
IEEE Transactions on Knowledge and Data Engineering
A Neural Network-Based Novelty Detector for Image Sequence Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel PCA for novelty detection
Pattern Recognition
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fast support vector data descriptions for novelty detection
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Statistical processes monitoring based on improved ICA and SVDD
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Network intrusion and fault detection: a statistical anomaly approach
IEEE Communications Magazine
Neural tree density estimation for novelty detection
IEEE Transactions on Neural Networks
A method for improving classification reliability of multilayer perceptrons
IEEE Transactions on Neural Networks
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Novelty detection is an important issue for practical industrial application, in which there is only normal operating data available in most cases. This paper proposes a systematic approach for novelty detection of mechanical components, using support vector data description (SVDD), a kernel approach for modeling the support of a distribution. To reduce the false alarm rate and increase the detection accuracy, a parameter optimization estimation scheme is proposed based on a grid search method that relies on the performance trade-off between the minimum fraction of support vectors and the maximum dual problem objective value. An evaluation value (E-value) chart based on the kernel distance for detection result is also designed to facilitate the decision visualization. To illustrate the effectiveness of the proposed method, novelty detection was applied to a particular kind of tapered roller bearing used in an industrial robot, which is investigated as a case study. The experimental results, in comparison to other methods, demonstrate that the proposed SVDD can conduct novelty detection of the monitored mechanical component effectively with higher accuracy.