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.