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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 ：
本发明公开了一种基于迁移学习的滚动轴承故障诊断方法。针对无标记数据滚动轴承故障诊断方法不考虑细粒度信息导致故障诊断精度低的问题，提出一种基于迁移学习和残差网络的TL－ResNet故障诊断模型。首先，采用残差网络提取源域数据和目标域数据深层特征；其次，应用迁移学习中的领域自适应方法，计算已标记源域数据和未标记目标域数据的局部最大均值差异，并将该差异和源域样本分类误差作为损失函数，使用反向传播算法对网络进行训练；最后，基于CWRU轴承数据进行了变负载迁移实验，实验结果表明该方法大幅提升了故障诊断模型诊断精度，更加符合实际工程应用需求。
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| GB/T 7714 | 高学金 , 李虎 . 一种基于迁移学习的滚动轴承故障诊断方法 : CN202211139882.0[P]. | 2022-09-20 . |
| MLA | 高学金 et al. "一种基于迁移学习的滚动轴承故障诊断方法" : CN202211139882.0. | 2022-09-20 . |
| APA | 高学金 , 李虎 . 一种基于迁移学习的滚动轴承故障诊断方法 : CN202211139882.0. | 2022-09-20 . |
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Abstract ：
本发明公开了一种基于注意力增强时频Transformer的滚动轴承剩余寿命预测方法。针对传统Transformer模型对振动信号的上下文区域信息不敏感，导致在时序预测中容易忽略重要信息、降低预测精度的问题，该方法首先提取原始振动信号的时域和频域统计特征来构建健康指标，以全面表征轴承退化信息；在此基础上，引入通道‑空间注意力模块对时域和频域特征进行高适配性特征融合，以提高模型输入特征的质量。其次，提出一种新型卷积多头自注意力机制以增强模型学习序列上下文区域信息的能力，充分捕获信息之间的局部关联性。最后，利用全连接层、GeLU激活函数和Sigmoid激活函数构建回归器对滚动轴承剩余寿命进行预测。本发明有效地学习信号特征与剩余寿命之间的复杂映射关系，实现滚动轴承高精度剩余寿命预测。
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| GB/T 7714 | 高慧慧 , 张潇然 , 韩红桂 et al. 一种基于注意力增强时频Transformer的滚动轴承剩余寿命预测方法 : CN202211670437.7[P]. | 2022-12-24 . |
| MLA | 高慧慧 et al. "一种基于注意力增强时频Transformer的滚动轴承剩余寿命预测方法" : CN202211670437.7. | 2022-12-24 . |
| APA | 高慧慧 , 张潇然 , 韩红桂 , 高学金 , 李方昱 . 一种基于注意力增强时频Transformer的滚动轴承剩余寿命预测方法 : CN202211670437.7. | 2022-12-24 . |
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Abstract ：
本发明公开了一种基于全局局部百分位数法的LSTM‑ED发酵过程故障预测方法。首先，对监测指标SPE进行归一化处理，然后分割阈值上下波动的时间段，对这些时间段使用全局百分位数方法进行处理，得出TSP所在的区间，然后在TSP所在的区间使用局部百分位数法进行处理，取该区间起始段的斜率最大值点即为TSP点。然后利用高斯误差线性单元模块对LSTM‑ED模型改进，最后将基于退化点TSP点的发酵数据代入基于GELU的LSTM‑ED模型中进行训练和测试，从而提高模型预测精度。
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| GB/T 7714 | 高学金 , 李学凤 , 韩华云 et al. 一种基于全局局部百分位数法的LSTM-ED发酵过程故障预测方法 : CN202211600709.6[P]. | 2022-12-12 . |
| MLA | 高学金 et al. "一种基于全局局部百分位数法的LSTM-ED发酵过程故障预测方法" : CN202211600709.6. | 2022-12-12 . |
| APA | 高学金 , 李学凤 , 韩华云 , 高慧慧 . 一种基于全局局部百分位数法的LSTM-ED发酵过程故障预测方法 : CN202211600709.6. | 2022-12-12 . |
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Abstract ：
本发明公开了一种基于注意力卷积神经网络的视觉里程计算法。针对传统的视觉里程计要求图片含有大量的纹理信息，且求解过程较为复杂，而基于卷积神经网络的视觉里程计精度较低的问题，提出基于注意力卷积神经网络和门控循环单元的视觉里程计。利用注意力机制提高卷积模块特征提取的精度，从而提高视觉定位的精度。相比于以往的视觉里程计算法，在保证了精度的同时摒弃了复杂的求解过程，更加适合于实际工程应用。
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| GB/T 7714 | 高学金 , 牟雨曼 , 任明荣 . 一种基于注意力卷积神经网络的视觉里程计算法 : CN202210113074.0[P]. | 2022-01-29 . |
| MLA | 高学金 et al. "一种基于注意力卷积神经网络的视觉里程计算法" : CN202210113074.0. | 2022-01-29 . |
| APA | 高学金 , 牟雨曼 , 任明荣 . 一种基于注意力卷积神经网络的视觉里程计算法 : CN202210113074.0. | 2022-01-29 . |
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