LAUSR

Root cause analysis (RCA) methods for effectively identifying the critical causes of abnormal processes have attracted attention because manufacturing processes have grown in scale and complexity. However, the existing methods for building automatic RCA models suffer from the disadvantage of typically requiring expert knowledge. In addition, without a dataset representing the causal relationship of multivariate processes, it is difficult to provide useful information for RCA. Although data-driven RCA methods have been proposed, most are based on classification models. Given that product quality is defined as a continuous variable in many manufacturing industries, classification models are limited in deriving root causes affecting the product quality level. In this article, we propose a regression model-based RCA method, which we call quality-discriminative localization, consisting of a convolutional neural network (CNN)-based activation mapping of multisensor signal data. In our proposed method, the CNN predicts the product quality of a continuous variable. Activation mapping then extracts causal maps that highlight significant sensor signals for each product. To identify the root causes, we generate a root cause map from the weighted sum of quality and causal maps. We consider root causes as locations of abnormal processes and processing times from localized activation scores on the root cause map. We experimentally demonstrate the usefulness of the proposed method with simulated data and real process data from a steel manufacturing process. Our results show that the proposed method successfully identifies root causes with distinct sensor signal patterns.