摘要
针对现代复杂生产过程中不能准确识别、分类多种故障的问题,提出一种改进的稀疏表示故障分类方法。该方法依据信号的稀疏表示来判断故障所属类别。其具体实现过程首先是利用K-均值奇异值分解(K-SVD)算法构造过完备字典,使其包含原信息的主要特征,再通过粒子群(PSO)算法有效地搜索并寻找稀疏分解中产生的在过完备字典范围中的最匹配原子,最后利用以该匹配原子为基础的稀疏表示结果实现对多故障问题的分类识别。运用数值仿真验证了该算法的可行性和有效性。同时,针对柴油机燃油系统的故障分类,将该方法与基于BP神经网络和SVM的分类识别方法进行比较,实验表明该算法在故障分类上具有更好的效果。
In order to solve the problem of multiple faults which can not be identified and classified accurately in modern complex production process, an improved sparse representation fault classification method was proposed. This method is based on the sparse representation of the signal to determine the fault categories. First, the specific implementation process utilizes K-Means Singular Value De-composition（K-SVD） algorithm to constructe over complete dictionary with main features in the original message, and then uses the particle swarm optimization（PSO） algorithm to search and find the most matching atom which is generated in sparse decomposition in the range of over complete dictionary. Finally, the results based on the sparse representation realizes classification and identification about multiple faults problem. The va- lidity and practicability of the proposed method is verified by numerical simulation. Meanwhile, the proposed method was compared with the methods based on the BP neural network and SVM classification through the fault classification of diesel engine fuel system. Experiments show that the algorithm has good effect on fault classification.
出处
《计算机科学》
CSCD
北大核心
2016年第12期302-306,共5页
Computer Science
基金
国家自然科学基金项目(61573137)资助
关键词
稀疏表示
K-均值奇异值分解算法
粒子群算法
故障分类
Sparse representation,K-SVD algorithm, Particle swarm optimization algorithm, Fault classification
