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题名: 变速器新产品故障特征提取与分类方法
其他题名: Fault feature extraction and classification method for new transmission
作者: 周晓锋 ; 史海波 ; 胡东平 ; 尚文利
作者部门: 自动化系统研究室
关键词: 阶次分析 ; 特征提取 ; 特征选择 ; 故障诊断 ; 变速器
刊名: 计算机集成制造系统
ISSN号: 1006-5911
出版日期: 2012
卷号: 18, 期号:4, 页码:761-767
收录类别: EI ; CSCD
产权排序: 1
摘要: 针对汽车变速箱原始故障特征向量维数过高导致的检测效率低、准确率低的问题,提出一种基于阶次分析理论的特征提取方法和基于遗传算法—反向传播神经网络的特征选择与分类方法。首先运用阶次分析理论提取变速器的阶次域特征,与时域特征共同组成特征向量集;然后将类内类间距离比与惩罚系数之和作为目标函数值,利用遗传搜索策略对特征向量集进行特征选择,得到特征子集;最后用反向传播神经网络算法进行故障分类,得到检测结果,并通过实验验证了所提出方法的有效性。
英文摘要: During the process of new transmission products quality inspecting,the original fault feature vector dimension was too high to cause the detection in a low efficiency rate and low accuracy rate.To solve this problem,a feature extraction method based on order analysis theory together with a feature selection and classification method based on Genetic Algorithm-Back Propagation(GA-BP)algorithm were presented.The transmission’s order domain feature was extracted by using the order analysis theory,and it was composed with the domain’s feature to form a feature vector set.The sum of the penalty factor and the distance ratio of inner-class and between-class was taken as the objective function value,the features of feature vector set were selected by Genetic Algorithm(GA),and the feature subset was obtained.Back-propagation neural network algorithmare was applied to classify the fault.The effectiveness of proposed algorithm was validated by experiments. 
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/9917
Appears in Collections:自动化系统研究室_期刊论文

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