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学者姓名：钟建华
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Abstract ：
Mechanical metastructures consisting of periodic cells with adjustable output force charactersitics and ranges have received increasing attention in recent years owing to its unique capability to tune mechanical properties such as stiffness and Poisson's ratio etc. In this paper, we present the design, simulation, and experimental characterization of a mechanical metastructure that realizes customized constant force output. The metastructure consists of periodic constant force units that are formed by combining a positive and negative stiffness element. Notably, the force unit also contains a unique flexure design with solid and hollow pins to reduce the lateral stress by 50%, which allows for precise control of the output force. By using a programmable design method, the force unit forms 2D and 3D metastructures via parallel and tendem stacking. Simulations were performed to optimize the design and predict the device performance. Finally, experiments were devised and performed to verify the simulation results of the metastructures. The promising results warrant the wide application of the new mechanical metastructure as well as the programmable design method, such as low-pass mechanical filters, noise and vibration cancellation devices etc.
Keyword ：
constant force constant force mechanical metastructures mechanical metastructures programmable metastructures programmable metastructures
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| GB/T 7714 | Zhong, Jianhua , Li, Jin , Ding, Bingxiao et al. Design and experimental verification of programmable metastructures based on constant force cells [J]. | SMART MATERIALS AND STRUCTURES , 2025 , 34 (1) . |
| MLA | Zhong, Jianhua et al. "Design and experimental verification of programmable metastructures based on constant force cells" . | SMART MATERIALS AND STRUCTURES 34 . 1 (2025) . |
| APA | Zhong, Jianhua , Li, Jin , Ding, Bingxiao , Chen, Shih-Chi . Design and experimental verification of programmable metastructures based on constant force cells . | SMART MATERIALS AND STRUCTURES , 2025 , 34 (1) . |
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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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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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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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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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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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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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在轴重式动态汽车衡的服役状态下，由于受到重型货车频繁的加卸载循环冲击，会导致其内部螺栓发生松弛脱落，针对这一问题，提出了一种基于莱维飞行改进粒子群算法优化的广义回归神经网络(LPSO-GRNN)的轴重式动态汽车衡螺栓松紧状态预测模型，并结合振动信号特征提取，将该模型应用于汽车衡螺栓松紧状态的预测。首先，研究并提取了螺栓不同松紧状态下输出振动信号的波形指标、峰值指标、脉冲指标、峭度指标等信号特征，并将其作为模型的共同输入特征向量；然后，采用莱维飞行提高了粒子群优化算法的寻优能力，通过产生随机步长，提高了算法的全局寻优能力，避免算法陷入局部最优值；利用改进的算法对广义回归神经网络(GRNN)的光滑因子进行了优化，得到了全局最优的光滑因子；最后，通过设计实验，分别使用广义回归神经网络(GRNN)、粒子群算法优化广义回归神经网络(PSO-GRNN)和LPSO-GRNN,以此来对螺栓松紧状态进行了预测，并将预测结果与实际情况进行了对比分析。实验结果表明：基于LPSO-GRNN建立的螺栓松紧状态预测模型，其预测准确率可达到95%。研究结果表明：该螺栓松紧状态预测模型可以有效提高汽车衡螺栓松紧预测的准确率，同时有效解决粒子群算法容易陷入局部最优收敛的问题。
Keyword ：
LPSO-GRNN预测模型 LPSO-GRNN预测模型 广义回归神经网络 广义回归神经网络 振动信号特征提取 振动信号特征提取 粒子群算法优化 粒子群算法优化 莱维飞行 莱维飞行 螺栓紧固 螺栓紧固 轴重式动态汽车衡 轴重式动态汽车衡
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| GB/T 7714 | 梁伟 , 陈志雄 , 欧阳忠杰 et al. 基于LPSO-GRNN模型的螺栓松紧状态预测研究 [J]. | 机电工程 , 2023 , 40 (11) : 1814-1822 . |
| MLA | 梁伟 et al. "基于LPSO-GRNN模型的螺栓松紧状态预测研究" . | 机电工程 40 . 11 (2023) : 1814-1822 . |
| APA | 梁伟 , 陈志雄 , 欧阳忠杰 , 龚晟炜 , 钟建华 , 钟舜聪 et al. 基于LPSO-GRNN模型的螺栓松紧状态预测研究 . | 机电工程 , 2023 , 40 (11) , 1814-1822 . |
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