摘要
稀疏分类直接把故障样本作为原子会造成分类字典相干性较高,进而影响稀疏分类精度,为此提出一种通过有效降低分类字典相干性来提高稀疏分类效果的优化算法。该方法首先通过传播聚类算法获取分类子字典的代表原子,然后基于极分解和子空间旋转法对子字典进行相干性优化。在某型发动机上的实验结果表明,该算法在低相干性字典上能够对怠速和2000r/min工况下的5种常见空燃比故障进行高精度识别。
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.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2017年第23期2773-2778,2784,共7页
China Mechanical Engineering
基金
国家自然科学基金资助项目(61305134)
关键词
稀疏分类
字典相干性
汽油发动机
空燃比故障识别
sparse representation classification
coherence of dictionary
gasoline engine
air-fuel ratio fault recognition
