科研之友
微信
新浪微博
Facebook
分享链接
摘要
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.
