时间序列P-控制图异常主题模式预测关键技术研究

Research on key techniques of time series p-control chart abnormal theme pattern prediction

为了对电能表制造过程进行质量控制,引入BP神经网络作为异常主题模式集合的分类工具,采用最长公共子序列算法和中心时间序列算法对异常主题模式集合进行相似性度量和故障特征提取。最后对7种出现频率较高的典型故障特征之间的关联进行分析,以判定不良品率上升的原因。结果表明此方法能够有重点地分析故障原因,对于提升电能表质量有重大的指导意义。

In order to control the quality of the electricity meter manufacturing process,BP neural network is introduced as the classification tool of the abnormal theme pattern set.The longest common subsequence algorithm and the central time series algorithm are adopted to measure the similarity and extract the fault features of the abnormal theme pattern set.Finally,the correlation between the seven typical fault characteristics with high frequency is analyzed to determine the cause of the increase in the defective rate.The results show that this method can focus on finding the cause of the fault and has important guiding significance for improving the quality of the electricity meter.