新知识检测
模式识别(心理学)
人工智能
计算机科学
支持向量机
往复式压缩机
非线性系统
故障检测与隔离
特征提取
新颖性
分类器(UML)
气体压缩机
工程类
量子力学
机械工程
物理
哲学
神学
执行机构
作者
Kun Feng,Zhinong Jiang,Wei He,Bo Ma
标识
DOI:10.1016/j.eswa.2011.04.060
摘要
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.
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