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Volume 13 Issue 4
Apr.  2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  >  13(4): 864-876
Y. He, Z. Wang, W. Liu, J. Fang, L. Chen, and Z. Song, “A Novel phase-aware neural network framework for fault detection in multiphase processes via feature augmentation and phase discrimination,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 4, pp. 864–876, Apr. 2026. doi: 10.1109/JAS.2025.125708
Citation: Y. He, Z. Wang, W. Liu, J. Fang, L. Chen, and Z. Song, “A Novel phase-aware neural network framework for fault detection in multiphase processes via feature augmentation and phase discrimination,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 4, pp. 864–876, Apr. 2026. doi: 10.1109/JAS.2025.125708
shu

A Novel Phase-Aware Neural Network Framework for Fault Detection in Multiphase Processes via Feature Augmentation and Phase Discrimination

doi: 10.1109/JAS.2025.125708
Funds:  This work was supported in part by the National Natural Science Foundation of China (NSFC) (62473103), the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
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    Abstract
  • The dynamic nature of multiphase processes presents significant challenges to industrial fault detection. Most existing fault detection methods for multiphase processes, which have been developed to focus on creating a local fault detector for each phase, are hindered by two key challenges. Firstly, accurately matching test samples to their respective phases proves difficult, which leads to what is known as the phase matching problem. Secondly, constructing a reliable fault detector becomes challenging when limited data is available for specific phases. To overcome these challenges, a novel phase-aware neural network (PANN) is proposed in this paper for multiphase fault detection. The PANN is composed of a feature augmentation module, an encoder, a phase discriminator, and a decoder. Multiscale convolutional neural networks are employed to construct the feature augmentation module, which is used to extract multiscale features from the input data. The pseudo labels, which capture knowledge of the multiphase process, are used during the training of the phase discriminator to address the phase matching issue. A joint loss function is designed to train the entire PANN by integrating the loss terms for phase discrimination and future sample prediction. Validation of the proposed PANN is carried out using a numerical example. To further assess its practical application, the PANN is tested on a penicillin fermentation process dataset. Experimental results demonstrate that the proposed PANN achieves higher fault detection rates compared to several popular models currently used for fault detection in multiphase processes.

     

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