Fault detection based on one-class deep learning for manufacturing applications limited to an imbalanced database

Abstract

Despite extensive studies for the industrial applications of deep learning, its actual usage in manufacturing sites has been extremely restrained by the difficulty in obtaining sufficient industrial data, especially for production failure cases. In this study, we introduced a fault-detection module based on one-class deep learning for imbalanced industrial time-series data, which consists of three submodules, namely, time-series prediction based on deep learning, residual calculation, and one-class classification using one-class support vector machine and isolation forest. Four different networks were used for the time-series prediction: multilayer perception (MLP), residual network (ResNet), long-short-term memory (LSTM), and ResNet-LSTM, each trained with the one-class data having only the production success cases. We adopted the residuals of the deep-learning prediction as an elaborated feature for the construction of the one-class classification. We also tested the fault-detection module with the actual mass production data of a die-casting process. By adopting the features elaborated by the deep-learning time-series prediction, we showed that the total accuracy of the one-class classification significantly improved from 90.0% to 96.0%. Especially for its capability to detect production failures, the accuracy improved from 84.0% to 96.0%. The area under the receiver operating characteristics (AUROC) also improved from 87.56% to 98.96%. ResNet showed the best performance for detecting production failures, whereas ResNet-LSTM produced better results for ensuring the production success. Our results suggest that the one-class deep learning is a promising approach for extracting important features from time-series data to realize a oneclass fault-detection module.