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Xiaogang Deng

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4 papers

1 author row

EAAI Journal 2025 Journal Article

Zero-shot learning augmented slow feature analysis for semantic-aware industrial process fault detection

Wenjie Yang
Xiaogang Deng
Lumeng Huang
Yuping Cao

Slow Feature Analysis (SFA) has shown considerable success in the field of industrial process fault detection. Nonetheless, due to its unsupervised nature, SFA relies solely on the normal training data and overlooks the incorporation of prior process knowledge, which consequently diminishes its efficacy in early fault detection. To mitigate this limitation, this paper introduces the concept of Zero-Shot Learning (ZSL) and proposes an improved SFA approach, referred to as ZSL-SFA. This novel method leverages fault semantic representations as auxiliary knowledge to enhance fault detection sensitivity in industrial process monitoring. The ZSL-SFA framework implements a dual-model collaborative monitoring system: (1) a primary SFA model is developed using normal operational data to capture the dynamic characteristics of the process; and (2) a semantic encoding mechanism, grounded in expert knowledge, is devised to build the auxiliary model, where a probabilistic attribute learner adaptively extracts semantic information from fault attribute descriptions, facilitating effective fault knowledge transfer through similarity analysis. The monitoring outcomes from both the primary and auxiliary models are integrated using a Bayesian fusion strategy, culminating in a comprehensive ZSL-SFA monitoring system. The main advantage of this method is its ability to fully exploit prior process knowledge to enhance the basic SFA model without the need for additional labeled fault samples. Experimental validations on the Tennessee-Eastman process simulation platform are performed to indicate that the proposed ZSL-SFA method surpasses the basic SFA method in terms of fault detection performance.

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EAAI Journal 2024 Journal Article

Deep one-class classification model assisted by radius constraint for anomaly detection of industrial control systems

Xiaogang Deng
Jiayan Li

Anomaly detection of industrial control systems (ICS) based on sensor data analytic is of utmost importance because ICS may suffer from various attacks leading to anomaly behaviors and even equipment failures. As an emerging deep learning technique, deep support vector data description (DeSVDD) has been successfully applied to ICS anomaly detection. Its advantage lies in only requiring normal data to train one-class classifier, which is suitable for actual industrial scenarios with extremely data imbalance of abundant normal data and scare abnormal data. However, this also results in its shortcoming of ignoring the valuable classification information hidden in the abnormal data. In order to overcome this issue, this paper proposes an improved DeSVDD method, called radius constraint DeSVDD (RC-DeSVDD). The proposed model constructs an anomaly detection model framework of abnormal data assisted DeSVDD, where a radius constraint is designed by considering the difference between normal and abnormal data. Further, considering the limited amount of abnormal data, a bi-directional generative adversarial network (BiGAN) is introduced to generate abnormal data. Experimental results on three benchmark datasets demonstrate the superiority of the proposed RC-DeSVDD anomaly detection method.

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EAAI Journal 2023 Journal Article

On confidence computation and calibration of deep support vector data description

Xiaogang Deng
Xianhui Jiang

Deep support vector data description (DeSVDD) is an emerging anomaly detection method based on the deep learning methodology. However, few studies take the confidence of DeSVDD predictions into account so that the present DeSVDD models cannot indicate the reliability degree of the anomaly detection results. In this paper, we enrich the theory of DeSVDD by building the model confidence definition and developing the corresponding calibration strategy. For one thing, by revisiting the methodology of DeSVDD-based anomaly detection, the confidence of detection results is presented to indicate if the prediction of DeSVDD is reliable. For another, we propose a modified power T -scaling strategy to smooth the anomaly scores of DeSVDD model and improve its calibration performance without changing the original detection results. Six open experiment datasets are used to perform the method testing and the experimental results confirm the effectiveness of our proposed calibration strategy.

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EAAI Journal 2023 Journal Article

TransCFD: A transformer-based decoder for flow field prediction

Jundou Jiang
Guanxiong Li
Yi Jiang
Laiping Zhang
Xiaogang Deng

The computational fluid dynamics (CFD) method is computationally intensive and costly, and evaluating aerodynamic performance through CFD is time-consuming and labor-intensive. For the design and optimization of aerodynamic shapes, it is essential to obtain aerodynamic performance efficiently and accurately. This paper proposed TransCFD, a Transformer-based decoding architecture for flow field prediction. The aerodynamic shape is parameterized and used as input to the decoder, which learns an end-to-end mapping between the shape and the flow fields. Compared with the CFD method, the TransCFD was evaluated to have a mean absolute error (MAE) of less than 1%, increase the speed by three orders of magnitude, and perform very well in generalization capability. The method simplifies the input requirements compared to most existing methods. Although the object of this work is a two-dimensional airfoil, the setup of this scheme is very general. TransCFD is promising for rapid aerodynamic performance evaluation, with potential applications in accelerating the aerodynamic design.

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