Abstract
It is important to predict critical alarms in the manufacturing process that can reduce the utilization rate of the production facility. In recent years, deep neural networks have been widely used for prediction of alarm types in the manufacturing processes. However, the existing deep neural network classifiers follow a closed-set assumption that all predictable categories should be learned in the training stage. For this reason, when an unknown alarm type comes into the classifier, it should be classified as one of the predefined alarm types. In the actual manufacturing processes, it is extremely difficult to collect all possible types of alarm data. Therefore, a model with open set recognition is required to identify unknown alarm types. In addition, because the alarm type data collected from production facilities occurs simultaneously, a multi-label classification model is necessary. In this study, we propose a multi-label open set recognition model combined with background data that can improve the ability to identify unknown alarm types. We demonstrated the usefulness and applicability of the proposed method by comparing it with existing open set classification methods using real process data obtained from an automobile industry.
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