Fault Diagnosis in Condition of Sample Type Incompleteness Using Support Vector Data Description. (8th June 2015)

• Record Type: Journal Article
• Authors: Yi, Hui; Mao, Zehui; Jiang, Bin; Bo, Cuimei; Liu, Yufang; Luo, Hui
• Other Names: Li Gang Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2015(2015)
• Publication Date: 2015-06-08
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2015/432651
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Faulty samples are much harder to acquire than normal samples, especially in complicated systems. This leads to incompleteness for training sample types and furthermore a decrease of diagnostic accuracy. In this paper, the relationship between sample-type incompleteness and the classifier-based diagnostic accuracy is discussed first. Then, a support vector data description-based approach, which has taken the effects of sample-type incompleteness into consideration, is proposed to refine the construction of fault regions and increase the diagnostic accuracy for the condition of incomplete sample types. The effectiveness of the proposed method was validated on both a Gaussian distributed dataset and a practical dataset. Satisfactory results have been obtained.