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学者姓名：高学金
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Abstract ：
The chiller is the core component of the heating, ventilation, and air conditioning (HVAC) system, such that the intelligent fault diagnosis of the chillers is of great significance for equipment safety and energy conservation. The working conditions of the chiller are complex and changeable, resulting in considerable differences in the distribution of their operating data. In addition, collecting fault data under various working conditions is also very costly. To address fault diagnosis challenges without labeled data for changing working conditions, this article proposes a domain adaptive adversarial clustering (DAAC) algorithm, which achieves fault diagnosis under dynamic distributions in evolving working conditions. Specifically, the residual model based on autoencoders is used to decouple features. Then, the feature representation can be obtained through the data encoding network and mapped to a unit sphere space. Simultaneously, a single-layer unbiased neural network classifier's weight matrix is initialized as the prototype. In addition, the target domain features are stored in a memory buffer (MB), and a nonparametric softmax classifier is adopted to calculate the similarity entropy between the target and prototypes. On this basis, the iterative adversarial optimization is carried out to achieve dynamic clustering of prototypes and target domain features. Experimental results on the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) RP-1043 dataset and HY-31C dataset show that the self-supervised adaptive clustering method exhibits excellent feature alignment, enabling high-precision fault diagnosis without labeled data for target conditions.
Keyword ：
Transfer learning Transfer learning Adaptation models Adaptation models Data mining Data mining Employee welfare Employee welfare Entropy Entropy similarity entropy similarity entropy Prototypes Prototypes Training Training chiller fault diagnosis chiller fault diagnosis clustering clustering unsupervised domain adaptation unsupervised domain adaptation Data models Data models Feature extraction Feature extraction Fault diagnosis Fault diagnosis Adversarial optimization Adversarial optimization
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| GB/T 7714 | Han, Huazheng , Gao, Xuejin , Han, Huayun et al. Self-Supervised Learning via Domain Adaptive Adversarial Clustering for Cross-Domain Chiller Fault Diagnosis [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
| MLA | Han, Huazheng et al. "Self-Supervised Learning via Domain Adaptive Adversarial Clustering for Cross-Domain Chiller Fault Diagnosis" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 74 (2025) . |
| APA | Han, Huazheng , Gao, Xuejin , Han, Huayun , Gao, Huihui , Qi, Yongsheng , Jiang, Kexin . Self-Supervised Learning via Domain Adaptive Adversarial Clustering for Cross-Domain Chiller Fault Diagnosis . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
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Abstract ：
This brief addresses the data-driven fault monitoring issues for nonlinear systems with dynamic operations by taking into account the key performance indicators (KPIs). To reach this objective, the KPIs related to the other variables of the nonlinear systems are first established by means of the Takagi-Sugeno fuzzy technique. Then, the input/output data model of the KPIs is constructed in fuzzy form. By applying the subspace-aided method, a fuzzy-model-based KPI predictor is obtained. On this basis, a data-driven realization algorithm of the KPI prediction residual generator is proposed with available system measurements. Towards KPI-related fault monitoring purpose, the control limit is computed by utilizing the kernel density estimation method, with which an online KPI-related monitoring scheme for dynamic nonlinear systems is presented. Compared with the existing methods, the merits of this scheme lie in that it can handle the nonlinear and dynamic characteristics of data, so as to improve the detection performance of KPI-related faults. Meanwhile, the KPI prediction model can achieve interpretability by introducing the fuzzy modeling. An experimental study on the ship propulsion system is finally given to demonstrate the developed results.
Keyword ：
data-driven data-driven ship propulsion system ship propulsion system fuzzy-model-based key performance indicators fuzzy-model-based key performance indicators dynamic nonlinear systems dynamic nonlinear systems Fault monitoring Fault monitoring
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| GB/T 7714 | Han, Huayun , Zhao, Dong , Gao, Xuejin . A Data-Driven Key Performance Indicator-Related Monitoring Scheme for Dynamic Nonlinear Systems [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS , 2024 , 71 (4) : 2074-2078 . |
| MLA | Han, Huayun et al. "A Data-Driven Key Performance Indicator-Related Monitoring Scheme for Dynamic Nonlinear Systems" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 71 . 4 (2024) : 2074-2078 . |
| APA | Han, Huayun , Zhao, Dong , Gao, Xuejin . A Data-Driven Key Performance Indicator-Related Monitoring Scheme for Dynamic Nonlinear Systems . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS , 2024 , 71 (4) , 2074-2078 . |
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Abstract ：
In actual engineering scenarios, bearing fault signals are inevitably overwhelmed by strong background noise from various sources. However, most deep-learning-based diagnostic models tend to broaden the feature extraction scale to extract rich fault features for bearing-fault identification under noise interference, with little attention paid to multi-timescale discriminative feature mining with adaptive noise rejection, which affects the diagnostic performance. Thus, a multi-timescale attention residual shrinkage network with adaptive global-local denoising (AMARSN) was proposed for rolling-bearing fault diagnosis by learning discriminative multi-timescale fault features from signals and fully eliminating noise components in the multi-timescale fault features. First, a multi-timescale attention learning module (MALMod) was developed to capture multi-timescale fault features and enhance their discriminability under noise interference. Subsequently, an adaptive global-local denoising module (AGDMod) was constructed to fully eliminate noise in multiscale fault features by constructing specific global-local denoising thresholds and designing an adaptive smooth soft thresholding function. Finally, end-toend bearing fault diagnosis tasks were realized using a softmax classifier located at the end of the AMARSN. The AMARSN was validated using two bearing datasets. The extensive results demonstrated that the AMARSN can mine more effective fault features from signals and achieve average diagnostic accuracies of 85.24% and 80.09% under different noise with different levels.
Keyword ：
Global-local noise elimination Global-local noise elimination Adaptive soft thresholding function Adaptive soft thresholding function Attention mechanism Attention mechanism Rolling-bearing fault diagnosis Rolling-bearing fault diagnosis Multi-timescale feature extraction Multi-timescale feature extraction
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| GB/T 7714 | Gao, Huihui , Zhang, Xiaoran , Gao, Xuejin et al. Multi-timescale attention residual shrinkage network with adaptive global-local denoising for rolling-bearing fault diagnosis [J]. | KNOWLEDGE-BASED SYSTEMS , 2024 , 304 . |
| MLA | Gao, Huihui et al. "Multi-timescale attention residual shrinkage network with adaptive global-local denoising for rolling-bearing fault diagnosis" . | KNOWLEDGE-BASED SYSTEMS 304 (2024) . |
| APA | Gao, Huihui , Zhang, Xiaoran , Gao, Xuejin , Li, Fangyu , Han, Honggui . Multi-timescale attention residual shrinkage network with adaptive global-local denoising for rolling-bearing fault diagnosis . | KNOWLEDGE-BASED SYSTEMS , 2024 , 304 . |
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Abstract ：
Fault diagnosis (FD) is crucial for ensuring the safety and stability of industrial processes. In real industrial processes, fault features in measurement data are prone to be misidentified due to the feature overlap, which hinders the effective FD. Besides, the large-scale characteristic of industrial processes also poses challenges for FD. To solve the above problems, a hierarchical coarse-to-fine FD method based on decision fusion of class-specific stacked autoencoders (DFCSSAEs) is proposed to achieve accurate FD by identifying overlapping features. The entire FD process is divided into three stages: sub-block division, coarse diagnosis, and fine diagnosis. In the sub-block division stage, for the large-scale characteristic, a sub-block division method based on prior knowledge and mutual information (MI) is proposed to divide the whole process into several new sub-blocks. On this basis, the faults with overlapping features in each sub-block are identified by leveraging the feature visualization technique and summarized into a new composite class. In the coarse diagnosis stage, several SAE-Softmax-based coarse FD models are established to achieve the targeted diagnosis of individual faults and the composite class fault. At the fine diagnosis stage, a weighted Dempster-Shafer (D-S) evidence theory is proposed to solve decision conflicts among different coarse FD models by assigning reasonable weights. Moreover, a new SAE-Softmax is established to diagnose the unclassifiable faults after the decision fusion and ultimately improve diagnostic accuracy. Finally, the effectiveness and advantages of our method are validated by the Tennessee Eastman process (TEP) and the real dataset of the PRONTO process. Experimental results demonstrate that our method achieves high FD rates of 0.962 and 0.997.
Keyword ：
Fault diagnosis Fault diagnosis Principal component analysis Principal component analysis Feature extraction Feature extraction Accuracy Accuracy industrial process industrial process Mutual information Mutual information large scale large scale stacked autoencoder (SAE) stacked autoencoder (SAE) Dempster-Shafer (D-S) evidence theory Dempster-Shafer (D-S) evidence theory Numerical models Numerical models Evidence theory Evidence theory fault diagnosis (FD) fault diagnosis (FD)
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| GB/T 7714 | Gao, Huihui , Zhang, Xiaoran , Gao, Xuejin et al. A Hierarchical Coarse-to-Fine Fault Diagnosis Method for Industrial Processes Based on Decision Fusion of Class-Specific Stacked Autoencoders [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Gao, Huihui et al. "A Hierarchical Coarse-to-Fine Fault Diagnosis Method for Industrial Processes Based on Decision Fusion of Class-Specific Stacked Autoencoders" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Gao, Huihui , Zhang, Xiaoran , Gao, Xuejin , Li, Fangyu , Han, Honggui . A Hierarchical Coarse-to-Fine Fault Diagnosis Method for Industrial Processes Based on Decision Fusion of Class-Specific Stacked Autoencoders . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
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Abstract ：
本发明公开了基于动态自适应域对抗网络的多工况工业过程故障诊断方法，包括：对过程数据进行标准化并取滑动窗，获得动态数据，利用源工况的标签动态数据和目标工况的无标签动态数据训练动态自适应域对抗网络，动态自适应域对抗网络包含特征提取器、标签分类器、全局域鉴别器和局部域鉴别器，全局域鉴别器对齐源工况和目标工况的边缘分布，局部域鉴别器对齐源工况和目标工况的条件分布，提出一种可学习参数自适应地评估边缘分布和条件分布的相对重要性，以更好地提取域不变特征，并提出一种共同中心损失，提高数据的类间可分性和类内紧密性，进一步提高故障诊断精度。标签分类器用于预测样本的故障类别。本发明为目标工况建立精确的故障诊断模型。
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| GB/T 7714 | 高慧慧 , 黄文杰 , 韩红桂 et al. 基于动态自适应域对抗网络的多工况工业过程故障诊断方法 : CN202310069395.X[P]. | 2023-01-13 . |
| MLA | 高慧慧 et al. "基于动态自适应域对抗网络的多工况工业过程故障诊断方法" : CN202310069395.X. | 2023-01-13 . |
| APA | 高慧慧 , 黄文杰 , 韩红桂 , 高学金 . 基于动态自适应域对抗网络的多工况工业过程故障诊断方法 : CN202310069395.X. | 2023-01-13 . |
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Abstract ：
本发明公开了一种基于注意力卷积自编码器的发酵过程故障监测方法。首先将发酵过程的三维数据展开成二维形式，并通过滑动窗采样得到模型的输入序列；之后设计了一种通道卷积注意力模块，并将通道卷积注意力模块融入卷积自编码器中。利用注意力卷积自编码器构建故障监测模型，利用重构误差构建平方预测误差监控统计量实现在线监测，再利用核密度估计方法确定该监控统计量的控制限。测试时先将测试样本进行标准化，然后再输入到模型中，计算出监控统计量的值，并与其控制限进行比较。若未超出控制限则表示系统正常；若超出控制限，则表示出现故障样本。本发明对故障的发生更加敏感，有利于及时发现故障，减少监测过程中误报警、漏报警现象的发生。
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| GB/T 7714 | 高学金 , 姚玉卓 . 一种基于注意力卷积自编码器的发酵过程故障监测方法 : CN202310309451.2[P]. | 2023-03-26 . |
| MLA | 高学金 et al. "一种基于注意力卷积自编码器的发酵过程故障监测方法" : CN202310309451.2. | 2023-03-26 . |
| APA | 高学金 , 姚玉卓 . 一种基于注意力卷积自编码器的发酵过程故障监测方法 : CN202310309451.2. | 2023-03-26 . |
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Abstract ：
Radial basis function neural network (RBFNN) has been widely used in industrial process modeling because of its strong approximation ability. However, many existing modeling methods aim at accuracy, but ignore the stability of mode. Therefore, this paper proposes a parameter optimization method of RBF neural network based on modified Levenberg-Marquardt (MLM-RBFNN) to ensure the stability of the network. Firstly, a typical sample mechanism with variance reduction is proposed, which can reduce the error of gradient estimation and use accurate gradient information to guide learning. Secondly, a modified LM optimization algorithm is proposed to optimize the parameters, which not only improve the convergence speed of the network, but also ensure the stability of the model. Finally, a multi-step updating rule based on a typical sample and a single sample is designed, which effectively reduces the sample bias introduced by a single sample. In order to prove the advantages of the MLM-RBFNN method proposed in this paper, experiments are carried out on three benchmark data sets and a practical wastewater treatment process application problem and compared with several existing methods. The results show that the proposed MLM-RBFNN method has good performance in both learning speed and stability.
Keyword ：
second-order online learning second-order online learning Levenberg-Marquardt algorithm Levenberg-Marquardt algorithm Radial basis function neural network Radial basis function neural network typical sample typical sample gradient approximation gradient approximation
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| GB/T 7714 | Yang, Yanxia , Wang, Pu , Gao, Xuejin et al. Research and application of RBF neural network based on modified Levenberg-Marquardt [J]. | JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING , 2022 , 22 (5) : 1597-1619 . |
| MLA | Yang, Yanxia et al. "Research and application of RBF neural network based on modified Levenberg-Marquardt" . | JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 22 . 5 (2022) : 1597-1619 . |
| APA | Yang, Yanxia , Wang, Pu , Gao, Xuejin , Gao, Huihui , Qi, Zeyang . Research and application of RBF neural network based on modified Levenberg-Marquardt . | JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING , 2022 , 22 (5) , 1597-1619 . |
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Abstract ：
In semiconductor etching processes, fault detection monitors the quality of wafers. However, the detailed dynamics in batch data are ignored in many traditional methods. In this paper, sequential image-based data visualization and fault detection, using bi-kernel t-distributed stochastic neighbor embedding (t-SNE), is proposed for semiconductor etching processes. In the proposed method, multi-modals, multi-phases, and abnormal samples in batches are visualized in two-dimensional maps. First, the batch data are restructured into sequential images and input to a convolutional autoencoder (CAE) to learn the abstract representation. Then, bi-kernel t-SNE is applied to visualize the CAE codes in two-dimensional maps. To reduce the computational burden and overcome the out-of-sample projection diffusion in bi-kernel t-SNE, data subsampling is used in the training procedure. Finally, a one-class support vector machine is employed to calculate the visualization control boundary, and a batch-wise index is presented for fault wafer detection. To demonstrate the feasibility and effectiveness of the proposed method, it was applied to two wafer etching datasets. The results indicate that the proposed method outperforms state-of-the-art methods in data visualization and fault detection.
Keyword ：
Etching Etching Transforms Transforms Convolutional autoencoder (CAE) Convolutional autoencoder (CAE) Kernel Kernel Indexes Indexes fault detection fault detection Training Training Data visualization Data visualization data visualization data visualization t-distributed stochastic neighbor embedding (t-SNE) t-distributed stochastic neighbor embedding (t-SNE) Fault detection Fault detection semiconductor manufacturing semiconductor manufacturing
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| GB/T 7714 | Zhang, Haili , Wang, Pu , Gao, Xuejin et al. Data Visualization and Fault Detection Using Bi-Kernel t-Distributed Stochastic Neighbor Embedding in Semiconductor Etching Processes [J]. | IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING , 2022 , 35 (3) : 522-531 . |
| MLA | Zhang, Haili et al. "Data Visualization and Fault Detection Using Bi-Kernel t-Distributed Stochastic Neighbor Embedding in Semiconductor Etching Processes" . | IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 35 . 3 (2022) : 522-531 . |
| APA | Zhang, Haili , Wang, Pu , Gao, Xuejin , Qi, Yongsheng , Gao, Huihui . Data Visualization and Fault Detection Using Bi-Kernel t-Distributed Stochastic Neighbor Embedding in Semiconductor Etching Processes . | IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING , 2022 , 35 (3) , 522-531 . |
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Abstract ：
This article is dedicated to studying realization issues of the stability performance-based nonlinear fault-tolerant control framework via Takagi-Sugeno (T-S)fuzzy models. To this end, the nonlinear fault-tolerant control strategy with an online fault detection system monitoring the system stability performance degradation induced by faults is first introduced by means of the stable image and kernel representations. On this basis, the T-S fuzzy models are applied to approximate the nonlinear system, and a design approach of the fuzzy observer-based controller is proposed for the system stabilization via the iterative linear matrix inequality method. With the controller gains, the fuzzy-model-based nominal stable image representation of the system is formulated, which leads to the generation of the input and output error signals. Then, with the reference signal and system input and output error signal data, a data-driven algorithm is given to online estimate the evaluation function defined in terms of system uncertainties and faults. By virtue of the L-2 input-output stability of the controller stable kernel representation, a threshold calculation method is presented and, thus, the stability performance-based fault detection system based on fuzzy models is realized. Furthermore, for fault-tolerant purpose, the fault-tolerant controller design is discussed, which aims to retain the system stability. Two examples are provided in the end to illustrate the proposed results.
Keyword ：
fault-tolerant control fault-tolerant control Takagi-Sugeno fuzzy models Takagi-Sugeno fuzzy models Uncertainty Uncertainty Observers Observers stability performance stability performance Takagi-Sugeno model Takagi-Sugeno model stable image and kernel representations stable image and kernel representations Fault tolerant systems Fault tolerant systems Actuators Actuators Fault detection system Fault detection system Fault tolerance Fault tolerance Nonlinear systems Nonlinear systems
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| GB/T 7714 | Han, Huayun , Han, Honggui , Zhao, Dong et al. Takagi-Sugeno Fuzzy Realization of Stability Performance-Based Fault-Tolerant Control for Nonlinear Systems [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2022 , 30 (10) : 4249-4261 . |
| MLA | Han, Huayun et al. "Takagi-Sugeno Fuzzy Realization of Stability Performance-Based Fault-Tolerant Control for Nonlinear Systems" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 30 . 10 (2022) : 4249-4261 . |
| APA | Han, Huayun , Han, Honggui , Zhao, Dong , Gao, Xuejin , Yang, Ying . Takagi-Sugeno Fuzzy Realization of Stability Performance-Based Fault-Tolerant Control for Nonlinear Systems . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2022 , 30 (10) , 4249-4261 . |
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Abstract ：
Dynamic channel pruning has been proved to be an effective method by dynamically adjusting the inference path to reduce the computing costs. However, in most existing work, the classification performance decreases rapidly with the increase of pruning rate because their pruning strategy weakens the representation ability of the model to a certain extent. To resolve this problem, a dynamic channel pruning method based on activation gate (DCPAG) is proposed, which can better maintain the classification performance while reducing the computing costs. First, a pipeline aiming for generating pruning strategy, namely channel pruning auxiliary (CPA) is proposed, which considers both the representation ability and computing costs. Second, the pruning strategy generated by CPA is embedded into dynamic rectifying linear unit (DyReLU) to form the embedded dynamic rectifying linear unit (EB-DyReLU), which achieves dynamic channel pruning while maintaining the representation capability. Third, each input sample was self-classified according to its identification difficulty during pruning, and additional training was given to hard samples to achieve better classification performance. Finally, some experiments are carried out on CIFAR-10 and ImageNet respectively to verify the effectiveness of DCPAG in accuracy and floating point of per second (FLOPs). The results show that the proposed method achieves better performance than other similar channel-based methods at the same pruning rate. Specifically, this method not only achieves 0.5-1.5% improvement in classification accuracy, but also reduces the computational costs by 5%-20%.
Keyword ：
Gates Gates ReLU-Activation ReLU-Activation Channel Channel Pruning-Dynamic Pruning-Dynamic
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| GB/T 7714 | Liu, Shun-Qiang , Yang, Yan-Xia , Gao, Xue-Jin et al. Dynamic channel pruning via activation gates [J]. | APPLIED INTELLIGENCE , 2022 , 52 (14) : 16818-16831 . |
| MLA | Liu, Shun-Qiang et al. "Dynamic channel pruning via activation gates" . | APPLIED INTELLIGENCE 52 . 14 (2022) : 16818-16831 . |
| APA | Liu, Shun-Qiang , Yang, Yan-Xia , Gao, Xue-Jin , Cheng, Kun . Dynamic channel pruning via activation gates . | APPLIED INTELLIGENCE , 2022 , 52 (14) , 16818-16831 . |
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