Flow sheet of TEP.

Flow sheet of TEP.

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At present, decision-making solutions developed based on deep learning (DL) models have received extensive attention in predictive maintenance (PM) applications along with the rapid improvement of computing power. Relying on the superior properties of shared weights and spatial pooling, convolutional neural networks (CNNs) can learn effective repre...

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... Tennessee Eastman process (TEP) is a simulation program developed from an actual chemical plant that has been widely employed in the verification of various health states recognition methods [53]. The TEP consists of five major units: reactor, condenser, compressor, separator, and stripper, as shown in Figure 3 [54]. The whole process can be described as: four gaseous reactants A, C, D, E and a small amount of inert B are sent into the reactor to form liquid products F, G, and H. ...

Citations

... Compared with traditional machine learning methods, deep learning based methods can automatically extract features and achieve high accuracy [18]. Convolutional neural network (CNN), deep belief network (DBN), stacked autoencoders (SAE), long short-term memory network (LSTM), and vision transformer (ViT) have been successfully applied to solve the fault diagnosis problems in process industry systems [19,20,21,22,23,24]. Hashim et al. combines a multi-Gaussian assumption and an attribute fusion network to enhance zero-shot fault diagnosis [25]. ...
... Feature visualization using T-SNE in task T2.24 ...
Preprint
Domain generalization achieves fault diagnosis on unseen modes. In process industrial systems, fault samples are limited, and only single-mode fault data can be obtained. Extracting domain-invariant fault features from single-mode data for unseen mode fault diagnosis poses challenges. Existing methods utilize a generator module to simulate samples of unseen modes. However, multi-mode samples contain complex spatiotemporal information, which brings significant difficulties to accurate sample generation. Therefore, double gradient reversal network (DGRN) is proposed. First, the model is pre-trained to acquire fault knowledge from the single seen mode. Then, pseudo-fault feature generation strategy is designed by Adaptive instance normalization, to simulate fault features of unseen mode. The dual adversarial training strategy is created to enhance the diversity of pseudo-fault features, which models unseen modes with significant distribution differences. Subsequently, domain-invariant feature extraction strategy is constructed by contrastive learning and adversarial learning. This strategy extracts common features of faults and helps multi-mode fault diagnosis. Finally, the experiments were conducted on Tennessee Eastman process and continuous stirred-tank reactor. The experiments demonstrate that DGRN achieves high classification accuracy on unseen modes while maintaining a small model size.