Multiscale Control Chart Pattern Recognition Using Histogram-Based Representation of Value and Zero-Crossing Rate

In the research of control chart pattern recognition (CCPR), most previous methods used a classifier to label abnormal CCPs. However, long-term control chart data often contains a large number of small abnormal patterns, with characteristics unlike those seen from a global view of the entire chart. There is also a high probability that local abnormal patterns are worthy of analysis. This article presents a novel multiscale control chart pattern recognition scheme, MS-CCPR, which does not focus on the classification of data from a single chart. Rather, the proposed scheme uses a proposed histogram-based data representation in conjunction with time series subsequence matching to identify abnormal patterns on various scales from a long series of control charts. Experimental results demonstrate the efficacy of the proposed framework in the efficient detection of chart patterns at various scales, outperforming the state-of-the-art time series subsequence matching algorithms.

Automated summary

This research introduces a novel multiscale control chart pattern recognition scheme, which uses histogram-based data representation and time series subsequence matching to identify abnormal patterns at various scales in long series of control charts. Experimental results show that this framework efficiently detects chart patterns at different scales and outperforms state-of-the-art time series subsequence matching algorithms.