Feature Selection by Merging Sequential Bidirectional Search into Relevance Vector Machine in Condition Monitoring

作     者：ZHANG Kui DONG Yu BALL Andrew 

作者机构：School of Economics & Management Wuhan Polytechnic University School of Information Engineering Communication University of China School of Computing & Engineering University of Huddersfield 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2015年第28卷第6期

页      面：1248-1253页

核心收录：

学科分类：0817[工学-化学工程与技术] 080202[工学-机械电子工程] 08[工学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0801[工学-力学（可授工学、理学学位）] 

基　　金：Supported by Humanities and Social Science Programme in Hubei Province,China(Grant No.14Y035) National Natural Science Foundation of China(Grant No.71203170) National Special Research Project in Food Nonprofit Industry(Grant No.201413002-2) 

主　　题：feature selection relevance vector machine sequential bidirectional search fault diagnosis 

摘      要：For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of the original engineering meaning. In this paper, other forms of dimensionality reduction technique（feature selection methods） are employed to identify machinery condition, based only on frequency spectrum data. Feature selection methods are usually divided into three main types： filter, wrapper and embedded methods. Most studies are mainly focused on the first two types, whilst the development and application of the embedded feature selection methods are very limited. This paper attempts to explore a novel embedded method. The method is formed by merging a sequential bidirectional search algorithm into scale parameters tuning within a kernel function in the relevance vector machine. To demonstrate the potential for applying the method to machinery fault diagnosis, the method is implemented to rolling bearing experimental data. The results obtained by using the method are consistent with the theoretical interpretation, proving that this algorithm has important engineering significance in revealing the correlation between the faults and relevant frequency features. The proposed method is a theoretical extension of relevance vector machine, and provides an effective solution to detect the fault-related frequency components with high efficiency.