基于自适应时序窗口加权k近邻的故障检测方法

为了解决在工业生产过程中时序和多阶段的问题，提出了一种基于正交局部保持投影(OLPP)和自适应时序窗口加权k近邻(ATSWKNN)的故障检测方法。首先，采用OLPP方法，在保持样本近邻关系的基础上，将原始数据投影到低维特征空间；其次，在特征空间中选取某类时序窗口，并计算时序平方距离；然后，将窗口内样本到其空间上近邻集的平均累积平方距离的倒数作为权重；最后, 构造统计量对过程进行监控。OLPP ATSWKNN通过时序信息的提取和窗口内权重的引入降低了过程的自相关性和解决了多阶段的统计差异问题。此外，自适应的窗口切换策略解决了阶段切换时统计指标异常的问题。通过对数值模拟过程和青霉素发酵过程的监控实验，检验了OLPP ATSWKNN的监控性能，监控结果显著优于经典方法。

Fault detection method based on adaptive timing sequence window weighted k nearest neighbors

In order to solve the timing sequence and multi stage problems in industrial production process, a fault detection method based on orthogonal local preserving projection(OLPP) and adaptive timing sequence window weighted k nearest neighbor (ATSWKNN) was proposed. Firstly, basing on maintaining the sample nearest neighbor relationship, the original data are projected into the low dimensional feature space by using OLPP. Secondly, a certain kind of timing window is selected in the feature space, and the timing sequence square distance is calculated. Then, the reciprocal of the average cumulative square distance between the sample in the window and its spatial nearest neighbor set is taken as the weight. Finally, statistics are constructed to monitor the process. OLPP ATSWKNN reduces the autocorrelation of process and solve the problem of multi stage statistical difference by extracting time series information and introducing weight within the window. In addition, the problem of abnormal statistical indicators during phase switching is solved by adaptive window switching strategy. The monitoring performance of OLPP ATSWKNN is verified by monitoring the numerical simulation process and penicillin fermentation process, and the monitoring results are significantly better than the classical methods.