Query：
学者姓名：钟建华
Refining：
Year
Type
Indexed by
Source
Complex
Co-
Language
Clean All
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Meta-learning has been widely applied and achieved certain results in few-shot cross-domain fault diagnosis due to its powerful generalization and robustness. However, existing meta-learning methods mainly focus on cross-domain fault diagnosis within the same machine, ignoring the fact that there are more significant domain distribution differences and sample imbalance problems between different machines, leading to poor diagnostic performance. This paper proposes a semi-supervised prototypical network with dual correction to address these issues. First, a dual-channel residual network is utilized to comprehensively extract sample features, capturing deep and shallow information. Then, correct the semi-supervised prototypical network by weighting the features and adding a shift term on support set samples and query set samples, respectively, to diminish its intra-class and extra-class bias. Meanwhile, a regularization term is introduced into the model to balance the distribution among different class prototypes, enhancing distinctiveness. Finally, few-shot cross-machine fault diagnosis experiments are conducted on three datasets to validate the method's effectiveness. Additionally, an interpretability analysis of the model is conducted using the gradient-weighted class activation mapping (Grad-CAM) technique to discern its primary regions of focus in the classification tasks.
Keyword ：
cross-domain cross-domain fault diagnosis fault diagnosis interpretability analysis interpretability analysis meta-learning meta-learning semi-supervised prototypical network semi-supervised prototypical network
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Liu, Guigang , Gu, Kairong , Jiang, Haifeng et al. A semi-supervised prototypical network with dual correction for few-shot cross-machine fault diagnosis [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2025 , 36 (4) . |
| MLA | Liu, Guigang et al. "A semi-supervised prototypical network with dual correction for few-shot cross-machine fault diagnosis" . | MEASUREMENT SCIENCE AND TECHNOLOGY 36 . 4 (2025) . |
| APA | Liu, Guigang , Gu, Kairong , Jiang, Haifeng , Zhong, Jianhua , Zhong, Jianfeng . A semi-supervised prototypical network with dual correction for few-shot cross-machine fault diagnosis . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2025 , 36 (4) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Accurate prediction of the remaining useful life (RUL) of rolling bearings in mechanical equipment is crucial for ensuring the reliable operation of equipment and implementing effective maintenance measures. It also plays a key role in safeguarding personnel and property by reducing the risk of failures. Recently, converting monitoring data into a graph structure to capture the mutual influence between samples has emerged as an innovative approach in RUL prediction. However, existing methods cannot effectively extract features from graph-structured data with varying receptive fields and establish strong dependencies between nodes. This research proposes a novel bearing RUL prediction model based on the multi-region hypergraph self-attention network (M-HGSAN) to address these challenges. Firstly, the original data is concatenated and resampled using the sliding window with width L, and the two-dimensional sample set is constructed by time domain feature and frequency domain feature, which enriches the diversity of samples. The multi-scale synchronous semi-shrink attention network (MSSSAN) is used to obtain different channel features and multi-region features from different receptive fields, which enhances the dependence between features. Secondly, a hypergraph selfattention network (HGSAN) is designed, which combines the advantages of a hypergraph neural network (HGNN) and a self-attention mechanism. Obtain the ability to learn higher-order correlation and key features between nodes. In addition, the data is fed into residual stacked gated recurrent units (RSGRU) and fully connected (FC) layers to capture the nodes' temporal sequence features and predict the bearings' RUL. Finally, model interpretability experiments are carried out with XAI technology to help us understand the influence of each feature on RUL. Experimental results demonstrate the effectiveness of the M-HGSAN model, highlighting its potential to significantly enhance predictive maintenance strategies in industrial applications, thereby improving equipment reliability and safety.
Keyword ：
Attention Attention GRU GRU Hypergraph neural network Hypergraph neural network Residual Residual RUL RUL
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhong, Jianhua , Jiang, Haifeng , Gu, Kairong et al. Remaining useful life prediction of rolling bearing based on multi-region hypergraph self-attention network [J]. | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2025 , 225 . |
| MLA | Zhong, Jianhua et al. "Remaining useful life prediction of rolling bearing based on multi-region hypergraph self-attention network" . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING 225 (2025) . |
| APA | Zhong, Jianhua , Jiang, Haifeng , Gu, Kairong , Zhong, Shuncong . Remaining useful life prediction of rolling bearing based on multi-region hypergraph self-attention network . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2025 , 225 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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.
Keyword ：
cross-domain cross-domain fault diagnosis fault diagnosis few-shot few-shot prototypical network prototypical network shuffle attention shuffle attention
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhong, Jianhua , Gu, Kairong , Jiang, Haifeng et al. A fine-tuning prototypical network for few-shot cross-domain fault diagnosis [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2024 , 35 (11) . |
| MLA | Zhong, Jianhua et al. "A fine-tuning prototypical network for few-shot cross-domain fault diagnosis" . | MEASUREMENT SCIENCE AND TECHNOLOGY 35 . 11 (2024) . |
| APA | Zhong, Jianhua , Gu, Kairong , Jiang, Haifeng , Liang, Wei , Zhong, Shuncong . A fine-tuning prototypical network for few-shot cross-domain fault diagnosis . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2024 , 35 (11) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
本发明涉及一种基于VMD多域特征与MEDA的滚动轴承跨工况故障诊断方法。包括：采集滚动轴承振动信号，构造源域数据和目标域数据；使用参数优化的VMD对源域数据和目标域数据进行VMD分解，提取其奇异值特征和排列熵特征，与原始信号的时域特征组成多域特征并进行归一化；利用GFK将源域数据和目标域数据变换到流形空间，获得流形特征；利用源域数据训练一个弱分类器，使用该弱分类器得到目标域伪标签；对流形特征进行动态分布对齐，初步得到分类器f；迭代修正目标域标签，直至收敛，返回最终分类器f；将不同工况样本输入训练好的模型分类器，判断滚动轴承运行状态。本发明可以仅使用一种工况数据，进而达到诊断不同转速、不同负载下的故障数据。
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 钟建华 , 林云树 , 陈禹荃 . 一种基于VMD多域特征与MEDA的滚动轴承跨工况故障诊断方法 : CN202210039616.4[P]. | 2022-01-13 00:00:00 . |
| MLA | 钟建华 et al. "一种基于VMD多域特征与MEDA的滚动轴承跨工况故障诊断方法" : CN202210039616.4. | 2022-01-13 00:00:00 . |
| APA | 钟建华 , 林云树 , 陈禹荃 . 一种基于VMD多域特征与MEDA的滚动轴承跨工况故障诊断方法 : CN202210039616.4. | 2022-01-13 00:00:00 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
