基于动态多向局部离群因子的在线故障检测*

针对化工间歇生产过程的多模态问题,为了提高故障检测性能,将滑动窗口技术与局部离群因子(LOF)算法相结合,提出了一种新的动态多向局部离群因子(Dynamic Multiway Local Outlier Factor,DMLOF)用于工业过程在线故障检测的方法。首先将间歇过程数据展开成二维数据,利用滑动窗口技术分别在时间片内运用局部离群因子算法计算LOF统计量,并利用核密度估计(KDF)确定控制限。其次,对于新来数据标准化处理后分别在相应窗口内投影,确定新数据的LOF统计量并与控制限比较进行故障检测。最后通过青霉素发酵过程的仿真实验结果验证了该算法的有效性。

On-line fault detection based on dynamic multiway local outlier factor

For the multi-modal problem in batch process of chemical industry, using moving window with local outlier factor (LOF) propose a new dynamic multiway local outlier factor method(DMLOF) for on-line fault detection of industry process, and the method can improve the performance of fault detection. Firstly, the approach unfold the batch dataset into a two dimensional dataset, then in the time slice using local outlier factor algorithm with moving window technology calculate the local outlier factor statistics, and using the kernel density estimation (KDF) determine the control limits. Secondly, the new data is projected in the corresponding window after standardized, and the local outlier factor statistics of new data is determined and compared with control limits for fault detection. Finally, the results of simulation experiment of penicillin fermentation process shows the validity of the algorithm.