A recognition and novelty detection approach based on Curvelet transform,nonlinear PCA and SVM with application to indicator diagram diagnosis |
| |
Authors: | Kun Feng Zhinong Jiang Wei He Bo Ma |
| |
Affiliation: | 1. School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;2. State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;3. School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;1. Mechanical Science and Engineering Institute, Northeast Petroleum University, Daqing 163318, China;2. NSF I/UCR Center for Intelligent Maintenance Systems, Department of Mechanical Engineering, University of Cincinnati, Cincinnati, OH 45221, USA |
| |
Abstract: | Indicator diagram plays an important role in the health monitoring and fault diagnosis of reciprocating compressors. Different shapes of indicator diagram indicate different faults of reciprocating compressor. A proper feature extraction and pattern recognition method for indicator diagram is significant for practical uses. In this paper, a novel approach is presented to handle the multi-class indicator diagrams recognition and novelty detection problems. When multi-class faults samples are available, this approach implements multi-class fault recognition; otherwise, the novelty detection is implemented. In this approach, the discrete 2D-Curvelet transform is adopted to extract the representative features of indicator diagram, nonlinear PCA is employed for multi-class recognition to reduce dimensionality, and PCA is used for novelty detection. Finally, multi-class and one-class support vector machines (SVMs) are used as the classifier and novelty detector respectively. Experimental results showed that the performance of the proposed approach is better than the traditional wavelet-based approach. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|