Fault detection and diagnosis based on transfer learning for multimode chemical processes Abstract   Fault detection and diagnosis (FDD) has been an active research field during the past several decades. Methods based on deep neural networks have made some important breakthroughs recently. However, networks require a large number of fault data for training. A chemical process may have several modes during production. Since fault is a low possibility event, some modes may have few fault data in history. Furthermore, collecting and annotating industrial data are extremely expensive and timeconsuming. With scarce or unlabeled fault data, networks cannot be effectively used for modeling. In this paper, we present a FDD method based on transfer learning for multimode chemical processes. To overcome the fault data rareness and no label issues in some modes, transfer learning transfers the knowledge from a source mode to a target mode for FDD. Tennessee Eastman (TE) process with five modes is utilized to verify the performance of our proposed method.

A new unsupervised data mining method based on the stacked autoencoder for chemical process fault diagnosis Abstract   Process monitoring plays an important role in chemical process safety management, and fault diagnosis is a vital step of process monitoring. Among fault diagnosis researches, supervised ones are inappropriate for industrial applications due to the lack of labeled historical data in real situations. Thereby, unsupervised methods which are capable of dealing with unlabeled data should be developed for fault diagnosis. In this work, a new unsupervised data mining method based on deep learning is proposed for isolating different conditions of chemical process, including normal operations and faults, and thus labeled database can be created efficiently for constructing fault diagnosis model. The proposed method mainly consists of three steps: feature extraction by the convolutional stacked autoencoder (SAE), feature visualization by the tdistributed stochastic neighbor embedding (tSNE) algorithm, and clustering. The benchmark Tennessee Eastman process (TEP) and an industrial hydrocracking instance are utilized to illustrate the effectiveness of the proposed data mining method.