Recognition Applications for Air Fuel Ratio Faults of Gasoline Engines Using Sparse Representation Classification Based on Optimization of Dictionary Coherence
- Publication Type:
- Journal Article
- Zhongguo Jixie Gongcheng/China Mechanical Engineering, 2017, 28 (23)
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© 2017, China Mechanical Engineering Magazine Office. All right reserved. Sparse representation classification directly took fault samples as atoms which would result in higher coherence of classification dictionary. Thus, accuracy of sparse classification would be affected. A new optimization algorithm was proposed to improve effectiveness of sparse classification by effectively reducing the coherence of classification dictionary herein. Firstly, the representative atom of each sub-dictionary was obtained by affinity propagation clustering algorithm. Secondly, all the sub dictionaries consisted of representative atoms were optimized based on polar decomposition and subspace rotation methods. The experimental results of an engine show that, the novelty classification algorithm achieves high accuracy of recognition for five common faults in idle and 2000 r/min operating conditions using the dictionary with lower coherence.
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