Novel Fault Class Detection Based on Novelty Detection Methods

详细信息    查看全文

• 作者：Jiafan Zhang ; Qinghua Yan ; Yonglin Zhang ; Zhichu Huang
• 刊名：Lecture Notes in Control and Information Sciences
• 出版年：2006
• 出版时间：2006
• 年：2006
• 卷：345
• 期：1
• 页码：982-987
• 全文大小：155 KB

文摘

The ability to detect a new fault class can be a useful feature for an intelligent fault classification and diagnosis system. In this paper, we adopt two novelty detection methods, the support vector data description (SVDD) and the Parzen density estimation, to represent known fault class samples, and to detect new fault class samples. The experiments on real multi-class bearing fault data show that the SVDD can give both high identification rates for the prescribed ‘unknown’ fault samples and the known fault samples, which shows an advantage over the Parzen density estimation method in our experiments, via choosing the appropriate SVDD algorithm parameters.