Deep generative model with time series-image encoding for manufacturing fault detection in die casting process

The increasing demand for advanced fault detection in manufacturing processes has encouraged the application of industrial intelligence based on deep learning. However, implementing deep learning technology at actual manufacturing sites remains challenging because the data acquired during the manufacturing process are not only unlabeled but also imbalanced time series data. In this study, we constructed semi-supervised manufacturing fault detection methods to deal with the imbalanced time series data obtained from manufacturing applications, based on recently proposed deep generative models: variational autoencoder-reconstruction along projection pathway (VAE-RaPP) and Fence generative adversarial network (Fence GAN). To apply a semi-supervised learning algorithm, 1000 labeled samples of good product were prepared. The deep generative models learned the features of good product from these labeled samples during training. Consequently, the model was sufficiently trained to distinguish good and defective product in unlabeled samples. Additionally, we converted the time series data acquired during the manufacturing process into images to improve the feature extraction capability of deep neural networks based on three encoding methods: Gramian angular difference field (GADF), Markov transition field (MTF), and recurrence plot (RP). The performance of these methods was then compared using four evaluation indicators: area under the receiver operating characteristic (AUROC), average precision (AP) score, precision-recall (PR) curve, and accuracy. The VAE-RaPP exhibited outstanding performance in all types of encoding methods when compared with the Fence GAN. This research provides a novel approach that combines the encoding of time series into images and deep generative models for manufacturing fault detection.