An improved weighted one class support vector machine for turboshaft engine fault detection. (September 2020)

• Record Type: Journal Article
• Authors: Zhao, Yong-Ping; Huang, Gong; Hu, Qian-Kun; Li, Bing
• Is Part Of: Engineering applications of artificial intelligence. Volume 94(2020)
• Publication Date: 2020-09
• Subjects: One-class support vector machine -- One-class classification -- Fault detection -- Turboshaft engine; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103796
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: One-class support vector machine (OC-SVM) is a common algorithm to solve one-class classification (OCC) problem. Weighted OC-SVM (WOC-SVM) is an improved algorithm based on OC-SVM, which assigns a weight to each sample through a specific weight calculation method so as to improve the robustness of the algorithm. The recently proposed WOC-SVM algorithm based on neighbors' distribution named as WOC-SVM(ND) is an easily understandable and effective algorithm. The weighting strategy of WOC-SVM(ND) is only related to the distribution of instance's k -nearest neighbors. In other words, the farther the distance between the instance and the boundary of the data distribution is, the more even the distribution of k -nearest neighbors is and the bigger the corresponding weight of the instance is. However, this weight calculation method is unreasonable to some extent. That is to say, it only considers the distribution angle of k -nearest neighbors, but does not consider the influence of the distance between k -nearest neighbors and the instance on the weight. Besides, WOC-SVM(ND) cannot effectively solve the problem which has complex dataset consisting of multiple clusters. The algorithm proposed in this paper can solve these two problems simultaneously, which is composed of two parts. One is an improved version on the basis of WOC-SVM(ND), and the other takes into account the distribution density of samples' k -nearest neighbors. Their linear combination makes the weighting strategy more reasonable. Experimental results on eight benchmark datasets show that the proposed algorithm is feasible and effective. Moreover, when the proposed algorithm is applied to the fault detection of turboshaft engine, an impressive effectiveness is obtained.