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学者姓名：钟建华
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Abstract ：
In response to the need for multiple complete bearing degradation datasets in traditional deep learning networks to predict the impact on individual bearings, a novel deep learning-based rolling bearing remaining life prediction method is proposed in the absence of fully degraded bearng data. This method involves processing the raw vibration data through Channel-wise Attention Encoder (CAE) from the Encoder-Channel Attention (ECA), extracting features related to mutual correlation and relevance, selecting the desired characteristics, and incorporating the selected features into the constructed Autoformer-based time prediction model to forecast the degradation trend of bearings' remaining time. The feature extraction method proposed in this approach outperforms CAE and multilayer perceptual-Attention Encoder in terms of feature extraction capabilities, resulting in reductions of 0.0059 and 0.0402 in mean square error, respectively. Additionally, the indirect prediction approach for the degradation trend of the target bearing demonstrates higher accuracy compared to Informer and Transformer models, with mean square error reductions of 0.3352 and 0.1174, respectively. This suggests that the combined deep learning model proposed in this paper for predicting rolling bearing life may be a more effective life prediction method deserving further research and application.
Keyword ：
Autoformer Autoformer deep learning deep learning rolling bearings rolling bearings
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| GB/T 7714 | Zhong, Jianhua , Li, Huying , Chen, Yuquan et al. Remaining Useful Life Prediction of Rolling Bearings Based on ECA-CAE and Autoformer [J]. | BIOMIMETICS , 2024 , 9 (1) . |
| MLA | Zhong, Jianhua et al. "Remaining Useful Life Prediction of Rolling Bearings Based on ECA-CAE and Autoformer" . | BIOMIMETICS 9 . 1 (2024) . |
| APA | Zhong, Jianhua , Li, Huying , Chen, Yuquan , Huang, Cong , Zhong, Shuncong , Geng, Haibin et al. Remaining Useful Life Prediction of Rolling Bearings Based on ECA-CAE and Autoformer . | BIOMIMETICS , 2024 , 9 (1) . |
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The outstanding performance of current machine-learning fault diagnosis methods is mainly attributed to the availability of a large amount of labeled training data. However, in practical dynamic weighing systems, the high costs and variability of operating conditions limit the availability of reliable training data, hampering engineering fault diagnoses in dynamic weighing systems. To address this issue, this study proposes a novel cross-sensor fault diagnosis method based on multi-feature fusion using a transfer component analysis (TCA)-weighted k-nearest-neighbor (WKNN) network. Using this method, time- and frequency-domain features are extracted from a laboratory-simulated set of fault data with small batches of real operational data. Source and target domain features are fused, and TCA is applied to map the source and target domain samples to a latent space using kernel functions to reduce the distribution differences among the samples. Finally, the WKNN is employed as a metric learner to enhance small-sample data matching and classification to improve diagnostic accuracy. The results show that with three samples per support set, the proposed method achieves a diagnostic accuracy of 93.33%. Compared with other approaches, the proposed method exhibits stronger generalizability for diagnostic knowledge transference from sensor to dynamic weighing failure data, effectively improving precision in on-site small-sample environments and reducing sample imbalances.
Keyword ：
cross-sensor cross-sensor dynamic weighing system dynamic weighing system fault diagnosis fault diagnosis multi-feature fusion multi-feature fusion transfer component analysis transfer component analysis weighted k-nearest neighbor algorithm weighted k-nearest neighbor algorithm
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| GB/T 7714 | Liang, Wei , Chen, Zhixiong , Zhong, Jianhua et al. Multi-feature fusion-based TCA-WKNN cross-sensor fault diagnosis method for dynamic weighing [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2024 , 35 (1) . |
| MLA | Liang, Wei et al. "Multi-feature fusion-based TCA-WKNN cross-sensor fault diagnosis method for dynamic weighing" . | MEASUREMENT SCIENCE AND TECHNOLOGY 35 . 1 (2024) . |
| APA | Liang, Wei , Chen, Zhixiong , Zhong, Jianhua , Liao, Huazhong , Zhong, Shuncong . Multi-feature fusion-based TCA-WKNN cross-sensor fault diagnosis method for dynamic weighing . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2024 , 35 (1) . |
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由于实际工况下的轴承退化数据有限，无法获得足够的退化数据来训练神经网络，在深度学习网络中很难得到好的预测结果，所以提出一种新的结合机器学习和统计数据驱动的方法。首先对原始振动信号做特征提取，通过集合经验模态分解奇异值分解(Ensemble Empirical Mode Decompositiont Singular Value Decomposition,EEMD+SVD)得到数十维特征，加上剩余寿命预测常用的诸如峭度、均值等有效特征，利用决策树筛选出15维特征；将所筛选特征进行双指数拟合并通过t分布随机近邻嵌入(t-distributed Stochastic Neighbor Embedding,t-SNE)将退化信号降维成线性趋势。线性退化趋势在预测上相比于指数趋势有更好的泛化性，同时预测准确度相比于指数模型支持向量回归(Support Vector Regression,SVR)和深度信念网络(Deep Belief Network,DBN)都有较高的提升。
Keyword ：
t-SNE t-SNE 剩余寿命预测 剩余寿命预测 双指数模型 双指数模型 特征提取 特征提取 轴承 轴承
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| GB/T 7714 | 钟建华 , 黄聪 , 钟舜聪 et al. 基于t-SNE降维方法的滚动轴承剩余寿命预测 [J]. | 机械强度 , 2024 , 46 (04) : 969-976 . |
| MLA | 钟建华 et al. "基于t-SNE降维方法的滚动轴承剩余寿命预测" . | 机械强度 46 . 04 (2024) : 969-976 . |
| APA | 钟建华 , 黄聪 , 钟舜聪 , 肖顺根 . 基于t-SNE降维方法的滚动轴承剩余寿命预测 . | 机械强度 , 2024 , 46 (04) , 969-976 . |
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The paper proposes an unsupervised deep convolutional dynamic joint distribution domain adaptive network model for the problem of bearing fault diagnosis under variable conditions, which involves missing labeling of target domain data and large differences in the distribution of source and target domain data. The model consists of the following steps: (1) converting the original vibration signal of the bearing into a time-frequency map representation and performing feature extraction on the labeled source domain samples and the unlabeled target domain samples by the deep convolutional feature extractor; (2) dynamically aligning the marginal distribution and conditional distribution of the two domain features by the marginal distribution adaptation module and the conditional distribution adaptation module, so that the trained network model can classify the unlabeled target domain samples accurately according to the label mapping relationship of the source domain samples; (3) validating the model on two rolling bearing datasets; (4) experiment with model interpretability in conjunction with XAI techniques to help us understand what the model actually does. The experimental results on two rolling bearing datasets show the validity of the proposed model.
Keyword ：
Deep domain adaption Deep domain adaption Fault diagnosis Fault diagnosis Rolling bearing Rolling bearing Variable conditions Variable conditions
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| GB/T 7714 | Zhong, Jianhua , Lin, Cong , Gao, Yang et al. Fault diagnosis of rolling bearings under variable conditions based on unsupervised domain adaptation method [J]. | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2024 , 215 . |
| MLA | Zhong, Jianhua et al. "Fault diagnosis of rolling bearings under variable conditions based on unsupervised domain adaptation method" . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING 215 (2024) . |
| APA | Zhong, Jianhua , Lin, Cong , Gao, Yang , Zhong, Jianfeng , Zhong, Shuncong . Fault diagnosis of rolling bearings under variable conditions based on unsupervised domain adaptation method . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2024 , 215 . |
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With the continuous development of computer technology, deep learning has been widely used in fault diagnosis and achieved remarkable results. However, in actual production, the problem of insufficient fault samples and the difference in data domains caused by different working conditions seriously limit the improvement of model diagnosis ability. In recent years, meta-learning has attracted widespread attention from scholars as one of the main methods of few-shot learning. It can quickly adapt to new tasks by training on a small number of samples. A fine-tuning prototypical network is proposed on meta-learning methods to address the challenges of fault diagnosis under few-shot and cross-domain. Firstly, the shuffle attention is used to enhance the feature extraction ability of the network and suppress irrelevant features. Then, the support set of the target domain is split into two parts: pseudo support set and pseudo query set, which are used to fine-tune the prototypical network and improve the model generalization. Finally, experiments are conducted on three rotating equipment datasets to verify the method’s effectiveness. © 2024 IOP Publishing Ltd.
Keyword ：
cross-domain cross-domain fault diagnosis fault diagnosis few-shot few-shot prototypical network prototypical network shuffle attention shuffle attention
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| GB/T 7714 | Zhong, J. , Gu, K. , Jiang, H. et al. A fine-tuning prototypical network for few-shot cross-domain fault diagnosis [J]. | Measurement Science and Technology , 2024 , 35 (11) . |
| MLA | Zhong, J. et al. "A fine-tuning prototypical network for few-shot cross-domain fault diagnosis" . | Measurement Science and Technology 35 . 11 (2024) . |
| APA | Zhong, J. , Gu, K. , Jiang, H. , Liang, W. , Zhong, S. . A fine-tuning prototypical network for few-shot cross-domain fault diagnosis . | Measurement Science and Technology , 2024 , 35 (11) . |
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To address coupling motion issues and realize large constant force range of microgrippers, we present a serial two-degree-of-freedom compliant constant forcemicrogripper (CCFMG) in this paper. To realize a large output displacement in a compact structure, Scott-Russell displacement amplification mechanisms, bridge-type displacement amplification mechanisms, and lever amplification mechanisms are combined to compensate stroke of piezoelectric actuators. In addition, constant force modules are utilized to achieve a constant force output. We investigated CCFMG's performances by means of pseudo-rigid body models and finite element analysis. Simulation results show that the proposed CCFMG has a stroke of 781.34 mu m in the X-direction and a stroke of 258.05 mu m in the Ydirection, and the decoupling rates in two directions are 1.1% and 0.9%, respectively. The average output constant force of the clamp is 37.49N. The amplification ratios of the bridge-type amplifier and the Scott-Russell amplifier are 7.02 and 3, respectively. Through finite element analysis-based optimization, the constant force stroke of CCFMG is increased from the initial 1.6 to 3 mm.
Keyword ：
compliant mechanism compliant mechanism constant force gripper constant force gripper FEA optimization FEA optimization fully decoupled fully decoupled
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| GB/T 7714 | Shan, Ye , Ding, Bingxiao , Zhong, Jianhua et al. Design and optimization of a decoupled serial constant force microgripper for force sensitive objects manipulation [J]. | ROBOTICA , 2023 , 41 (7) : 2064-2078 . |
| MLA | Shan, Ye et al. "Design and optimization of a decoupled serial constant force microgripper for force sensitive objects manipulation" . | ROBOTICA 41 . 7 (2023) : 2064-2078 . |
| APA | Shan, Ye , Ding, Bingxiao , Zhong, Jianhua , Li, Yangmin . Design and optimization of a decoupled serial constant force microgripper for force sensitive objects manipulation . | ROBOTICA , 2023 , 41 (7) , 2064-2078 . |
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准确地预测轴承的剩余寿命是确保旋转设备可靠运行的关键.随着信息技术的发展,越来越多的研究人员将深度学习的方法应用于预测轴承剩余使用寿命.轴承在不同健康状态下全寿命周期的振动信号往往呈现出不同的数据分布,且其振动信号通常表现出非平稳的特性,这使得常规端到端的深度学习方法难以实现轴承剩余寿命的准确预测.为解决上述问题,通过提取轴承原始振动信号的均方根、峭度值、样本熵,并利用Gath-Geva(GG)模糊聚类算法实现轴承全寿命退化阶段的无监督划分.采用同步压缩小波变换提取轴承振动信号的时频图,以同时从时域、频域揭示轴承当下运行状态.相比较连续小波变换,该方法提取到的时频图有更高的分辨率及更低的噪声.笔者提出双通道卷积长短时记忆网络模型,能够有效提取时间序列图像的时空结构信息.通过利用西交大轴承数据集(XJTU-SY轴承数据集)进行试验,验证了所提方法在预测轴承剩余寿命上的有效性,为轴承剩余寿命预测提供了一种新思路.
Keyword ：
剩余寿命预测 剩余寿命预测 同步压缩小波变换 同步压缩小波变换 滚动轴承 滚动轴承 衰退阶段划分 衰退阶段划分 长短时记忆网络 长短时记忆网络
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| GB/T 7714 | 钟建华 , 高杨 , 钟舜聪 . 基于SWT和双通道ConvLSTM的滚动轴承剩余寿命预测 [J]. | 矿山机械 , 2023 , 51 (3) : 46-53 . |
| MLA | 钟建华 et al. "基于SWT和双通道ConvLSTM的滚动轴承剩余寿命预测" . | 矿山机械 51 . 3 (2023) : 46-53 . |
| APA | 钟建华 , 高杨 , 钟舜聪 . 基于SWT和双通道ConvLSTM的滚动轴承剩余寿命预测 . | 矿山机械 , 2023 , 51 (3) , 46-53 . |
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为了研究一个典型的风力机塔筒在风-地震耦合作用下的结构性能。通过有限元软件ABAQUS建立塔筒的精细模型，并对其进行非线性时程分析。其中，风载荷基于IECKaimal谱，根据叶素动量理论(BEM)，由风力机载荷计算软件Ashes输出叶片载荷；且将运行状态分为正常运行与故障运行。同时，为了研究不同周期地震波作用下塔筒的结构响应以及失效形式，将地震运动分为两组，一组为短周期地震，另一组为长周期地震。研究发现在风-地震耦合作用下塑性铰首先出现在风力机塔分段连接处，这与风单独作用时失效位置相同；且在短周期地震作用下一旦形成塑性铰将很快导致塔筒整体倒塌；当地震波峰值加速度(PGA)相近时，长周期地震对运行中风力机塔的结构性能影响较大。
Keyword ：
倒塌分析 倒塌分析 故障运行 故障运行 非线性时程分析 非线性时程分析 风力机塔筒 风力机塔筒 风-地震耦合 风-地震耦合
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| GB/T 7714 | 钟建华 , 王永涛 , 卢宗兴 et al. 风力机塔筒在风-地震耦合作用下的非线性时程分析 [J]. | 制造业自动化 , 2023 , 45 (04) : 159-164 . |
| MLA | 钟建华 et al. "风力机塔筒在风-地震耦合作用下的非线性时程分析" . | 制造业自动化 45 . 04 (2023) : 159-164 . |
| APA | 钟建华 , 王永涛 , 卢宗兴 , 陈禹荃 , 叶锦华 . 风力机塔筒在风-地震耦合作用下的非线性时程分析 . | 制造业自动化 , 2023 , 45 (04) , 159-164 . |
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To address the problem of insufficient real-world data on planetary gearboxes, which makes it difficult to diagnose faults using deep learning methods, it is possible to obtain sufficient simulation fault data through dynamic simulation models and then reduce the difference between simulation data and real data using transfer learning methods, thereby applying diagnostic knowledge from simulation data to real planetary gearboxes. However, the label space of real data may be a subset of the label space of simulation data. In this case, existing transfer learning methods are susceptible to interference from outlier label spaces in simulation data, resulting in mismatching. To address this issue, this paper introduces multiple domain classifiers and a weighted learning scheme on the basis of existing domain adversarial transfer learning methods to evaluate the transferability of simulation data and adaptively measure their contribution to label predictor and domain classifiers, filter the interference of unrelated categories of simulation data, and achieve accurate matching of real data. Finally, partial transfer experiments are conducted to verify the effectiveness of the proposed method, and the experimental results show that the diagnostic accuracy of this method is higher than existing transfer learning methods.
Keyword ：
dynamics simulation dynamics simulation fault diagnosis fault diagnosis partial transfer learning partial transfer learning planetary gearbox planetary gearbox
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| GB/T 7714 | Song, Mengmeng , Xiong, Zicheng , Zhong, Jianhua et al. Fault Diagnosis of Planetary Gearbox Based on Dynamic Simulation and Partial Transfer Learning [J]. | BIOMIMETICS , 2023 , 8 (4) . |
| MLA | Song, Mengmeng et al. "Fault Diagnosis of Planetary Gearbox Based on Dynamic Simulation and Partial Transfer Learning" . | BIOMIMETICS 8 . 4 (2023) . |
| APA | Song, Mengmeng , Xiong, Zicheng , Zhong, Jianhua , Xiao, Shungen , Ren, Jihua . Fault Diagnosis of Planetary Gearbox Based on Dynamic Simulation and Partial Transfer Learning . | BIOMIMETICS , 2023 , 8 (4) . |
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针对换衣行人重识别(ReID)任务中有效信息提取困难的问题,提出一种基于语义引导自注意力网络的换衣ReID模型.首先,利用语义信息将图像分割出无服装图像,和原始图像一起输入双分支多头自注意力网络进行计算,分别得到衣物无关特征和完整行人特征.其次,利用全局特征重建模块(GFR),重建两种全局特征,得到的新特征中服装区域包含换衣任务中鲁棒性更好的头部特征,使得全局特征中的显著性信息更突出;利用局部特征重组重建模块(LFRR),在完整图像特征和无服装图像特征中提取头部和鞋部局部特征,强调头部和鞋部特征的细节信息,并减少换鞋造成的干扰.最后,除了使用行人重识别中常用的身份损失和三元组损失,提出特征拉近损失(FPL),拉近局部与全局特征、完整图像特征与无服装图像特征之间的距离.在PRCC(Person ReID under moderate Clothing Change)和VC-Clothes(Virtually Changing-Clothes)数据集上,与基于衣物对抗损失(CAL)模型相比,所提模型的平均精确率均值(mAP)分别提升了4.6和0.9个百分点;在Celeb-reID和Celeb-reID-light数据集上,与联合损失胶囊网络(JLCN)模型相比,所提模型的mAP分别提升了0.2和 5.0个百分点.实验结果表明,所提模型在换衣场景中突出有效信息表达方面具有一定优势.
Keyword ：
多头自注意力网络 多头自注意力网络 换衣行人重识别 换衣行人重识别 特征重建 特征重建 特征重组 特征重组 语义分割 语义分割
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| GB/T 7714 | 钟建华 , 邱创一 , 巢建树 et al. 基于语义引导自注意力网络的换衣行人重识别模型 [J]. | 计算机应用 , 2023 , 43 (12) : 3719-3726 . |
| MLA | 钟建华 et al. "基于语义引导自注意力网络的换衣行人重识别模型" . | 计算机应用 43 . 12 (2023) : 3719-3726 . |
| APA | 钟建华 , 邱创一 , 巢建树 , 明瑞成 , 钟剑锋 . 基于语义引导自注意力网络的换衣行人重识别模型 . | 计算机应用 , 2023 , 43 (12) , 3719-3726 . |
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