A recognition and novelty detection approach based on Curvelet transform, nonlinear PCA and SVM with application to indicator diagram diagnosis

作者：Feng, Kun; Jiang, Zhinong^*; He, Wei; Ma, Bo

来源：Expert Systems with Applications, 2011, 38(10): 12721-12729.

DOI：10.1016/j.eswa.2011.04.060

• 2D-Curvelet transform
• Nonlinear PCA
• SVM
• Novelty detection
• Indicator diagram recognition
• Reciprocating compressor

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.

• 出版日期2011-9-15
• 单位北京化工大学