Synchronization of batch trajectories using dynamic time warping
Synchronization of batch trajectories using dynamic time warping
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DOI：
10.1002/aic.690440412
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发表时间：
1998-04-01
期刊：
影响因子：
3.7
通讯作者：
Taylor, PA
中科院分区：
文献类型：
--
作者：
Kassidas, A;MacGregor, JF;Taylor, PA
提出了动态时间翘曲（DTW）在批处理过程的分析和监视中的应用。这种基于动态的基于动态的技术已用于识别孤立和连接的单词的语音识别领域。 DTW具有通过适当地翻译扩展并在两个轨迹内收缩局部段来同步两个轨迹的能力，以达到轨迹之间的最小距离。批处理过程通常以非同步轨迹为特征，这是由于存在批处理干扰和物理约束的存在。为了比较这些批次历史并应用统计分析，需要调解这些轨迹之间的时序差异。这可以使用DTW只有最少的过程知识来实现。 DTW和基于多线PCA/PLS的监视方法的组合用于离线和在线实现。来自工业聚合反应器的数据用于说明该方法的实现和性能。
The application of dynamic time warping (DTW) to the analysis and monitoring of batch processes is presented. This dynamic-programming-based technique has been used in the area of speech recognition for the recognition of isolated and connected words. DTW has the ability to synchronize two trajectories by appropriately translating expanding, and contracting localized segments within both trajectories to achieve a minimum distance between the trajectories. Batch processes often are characterized by unsynchronized trajectories, due to the presence of batch-to-batch disturbances and the existence of physical constraints. To compare these batch histories and apply statistical analysis one needs to reconcile the timing differences among these trajectories. This can be achieved using DTW with only a minimal amount of process knowledge. The combination of DTW and a monitoring method based on Multiway PCA/PLS is used for both off-line and on-line implementation. Data from an industrial polymerization reactor are used to illustrate the implementation and the performance of this method.