Towards Reliable Recognition of Concurrent Abnormal Patterns in Control Charts Using Multi-Label Deep Learning

Abstract

Control charts do more than raise an alarm: their shapes can give an early indication of what has changed in a process. This study considers the case in which one chart window contains more than one abnormal behavior. The observed sequence is then a mixture rather than a pure pattern. We formulate this problem directly as multi-label classification. A one-dimensional CNN receives a raw-scale window of 32 observations and predicts the active elementary labels. The controlled protocol contains twelve scenarios: normal behavior, six single abnormal patterns, and five selected concurrent patterns. Raw-scale input is retained because shift patterns depend partly on level information that may be weakened by window-wise normalization. The retained training setup gives additional exposure to difficult shift and trend cases, while validation and testing remain balanced. Across five repeated trainings, the model achieved 96.11% exact match accuracy, 96.41% precision, 96.46% recall, 96.44% F1-score, and 1.04% Hamming loss. The 95% confidence interval for exact match was 96.05–96.17%. Additional analyses show stable performance around a decision threshold of 0.5, strong cyclic and systematic recognition, and lower performance for short shift cases. The results support direct multi-label CNN recognition for the selected protocol. Broader shift-containing mixtures, more complex combinations, varying noise conditions, and real industrial validation remain outside the scope of the present controlled study.