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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 ：
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 ：
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 ：
In modern plants, industrial processes generally operate under different modes, and reliable monitoring for such processes is highly important. One of the key challenges is how to accurately identify the various modes including steady modes and transitions modes. In this paper, a novel monitoring scheme based on hierarchical mode identification strategy and stacked denoising autoencoder (HMI-SDAE) is proposed for multimode processes. First, a novel mode division strategy called HMI is presented. In HMI, the Gaussian mixture model (GMM) is adopted to realize the preliminary identification of various modes by extracting the global distribution features of the variables. In this way, the whole multimode process is divided into multiple steady modes. An improved density peaks clustering algorithm based on local density relation search (LDRSDPC) is proposed to achieve the transition mode identification by fully utilizing the local distribution features of the process variables involved in any two adjacent steady modes and transition mode between them. A decision criterion combined with local density relation is constructed to automatically determine the clustering center. In this hierarchical way, multiple steady modes and transition modes are divided automatically and accurately. Secondly, the deep nonlinear features embedded in process variables are mined by SDAE, and the robust monitoring model is established for each steady mode. A monitoring statistic is constructed using the reconstruction error for detecting faults. The effectiveness and feasibility of the proposed HMI-SDAE monitoring scheme are illustrated with a numerical example and Tennessee Eastman (TE) process.(c) 2022 Elsevier Ltd. All rights reserved.
Keyword ：
Density peaks clustering Density peaks clustering Gaussian mixture model Gaussian mixture model Multimode process monitoring Multimode process monitoring Mode identification Mode identification Industrial process Industrial process Stacked denoising autoencoder Stacked denoising autoencoder
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| GB/T 7714 | Gao, Huihui , Wei, Chen , Huang, Wenjie et al. Multimode process monitoring based on hierarchical mode identification and stacked denoising autoencoder [J]. | CHEMICAL ENGINEERING SCIENCE , 2022 , 253 . |
| MLA | Gao, Huihui et al. "Multimode process monitoring based on hierarchical mode identification and stacked denoising autoencoder" . | CHEMICAL ENGINEERING SCIENCE 253 (2022) . |
| APA | Gao, Huihui , Wei, Chen , Huang, Wenjie , Gao, Xuejin . Multimode process monitoring based on hierarchical mode identification and stacked denoising autoencoder . | CHEMICAL ENGINEERING SCIENCE , 2022 , 253 . |
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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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Abstract ：
Key performance indicators (KPI)-related process monitoring has been of great significance to ensure product quality and economic benefits for batch processes. Considering that different phases exhibit different characteristics, one of the key issues is how to partition the whole batch process into different phases and characterize them separately by multiple phase models. In order to model and monitor batch processes more accurately and efficiently, a novel canonical correlation analysis (CCA) strategy is proposed in this paper. The phase partition algorithm is designed based on the joint canonical variable matrix (JCVM). Different from previous methods, it considers the time sequence of operation phases and can distinguish the phase switches from dynamics anomalies. Using this algorithm, phases are separated in order from a KPI-related perspective, revealing high correlation among variables. After phase partition, a novel multi-phase local neighbourhood standardization CAA (MPLNSCCA) method focusing on KPI is set up for online monitoring, which could further address the misclassification problems. The advantages of the proposed method are illustrated by two case studies, a penicillin simulation platform and an industrial application of Escherichia coli fermentation, respectively.
Keyword ：
key performance indicator key performance indicator process monitoring process monitoring batch process batch process phase partition phase partition
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| GB/T 7714 | Gao, Xuejin , He, Zihe , Gao, Huihui et al. Phase partition and KPI-related process monitoring for batch processes using a novel canonical correlation analysis strategy [J]. | CANADIAN JOURNAL OF CHEMICAL ENGINEERING , 2022 , 101 (4) : 1967-1985 . |
| MLA | Gao, Xuejin et al. "Phase partition and KPI-related process monitoring for batch processes using a novel canonical correlation analysis strategy" . | CANADIAN JOURNAL OF CHEMICAL ENGINEERING 101 . 4 (2022) : 1967-1985 . |
| APA | Gao, Xuejin , He, Zihe , Gao, Huihui , Qi, Yongsheng . Phase partition and KPI-related process monitoring for batch processes using a novel canonical correlation analysis strategy . | CANADIAN JOURNAL OF CHEMICAL ENGINEERING , 2022 , 101 (4) , 1967-1985 . |
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