Semi-supervised LSTM ladder autoencoder for chemical process fault diagnosis and localization

Abstract

Deep learning is attracting widespread attention in the field of chemical process fault diagnosis recently. However, most deep learning methods are based on supervised learning and heavily rely on labeled data, with massive unlabeled data underutilized. Moreover, these supervised deep learning methods are uninterpretable and cannot facilitate fault localization, which is necessary for supervising the process back to normal. In this study, long short-term memory (LSTM) is used to extract temporal features and ladder autoencoder (LAE) is adopted for semi-supervised learning. Combining LSTM and LAE, LSTM-LAE is innovatively proposed to effectively utilize unlabeled data, with fault diagnosis performance largely improved. Moreover, LSTM-LAE achieves the interpretability to extract fault-relevant process variables with its elaborately designed internal features. When applied on a continuous stirred tank heater and the benchmark Tennessee Eastman process, LSTM-LAE exhibited a state-of-the-art fault diagnosis performance and localized faults to their relevant variables correctly.