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International Journal of Hydromechatronics最新文献

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Fault diagnosis of hydraulic system based on D-S evidence theory and SVM 基于D-S证据理论和支持向量机的液压系统故障诊断
IF 5.1 Q1 Engineering Pub Date : 2023-01-01 DOI: 10.1504/ijhm.2023.10056122
Lintao Wang, Wushuang Sun, H. Yin, Yongfeng Wang
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引用次数: 0
Influence of oil temperature on pressure distribution and flow force of valve core 油温对阀芯压力分布和流动力的影响
IF 5.1 Q1 Engineering Pub Date : 2023-01-01 DOI: 10.1504/ijhm.2023.10056525
Shuyun Qiao, A. A. Shah, C. Hong
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引用次数: 0
Hydraulic optimisation design of axial flow pump based on blade velocity distribution 基于叶片速度分布的轴流泵水力优化设计
Q1 Engineering Pub Date : 2023-01-01 DOI: 10.1504/ijhm.2023.134336
Daqing Qin, Xidong Wu, Jianguang Zhang, Xiaohang Wang, Na Yan
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引用次数: 0
Influence of oil temperature on pressure distribution and flow force of valve core 油温对阀芯压力分布和流动力的影响
Q1 Engineering Pub Date : 2023-01-01 DOI: 10.1504/ijhm.2023.134337
Conghua Hong, Shuyun Qiao, Aamer Abbas Shah
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引用次数: 0
A novel tool condition monitoring based on Gramian angular field and comparative learning 一种基于Gramian角场和比较学习的刀具状态监测方法
Q1 Engineering Pub Date : 2023-01-01 DOI: 10.1504/ijhm.2023.130510
Hongche Wang, Wei Sun, Weifang Sun, Yan Ren, Yuqing Zhou, Qijia Qian, Anil Kumar
Accurate tool condition monitoring (TCM) is an important part for ensuring milling quality. However, due to the cost of TCM experiment, there are few labelled and a lot of unlabelled samples in the training set that significantly affect the accuracy of many machine learning models. A novel method based on comparative learning (CL) and Gramian angular field (GAF) is proposed for improving the performance of TCM. The cutting force signals of each channel of all samples (including labelled and unlabelled) collected in TCM experiment are expanded to grey images by GAF, and combined with other channels to a colour image. Then, these colour images are input to the CL pre-training model to learn features. Finally, the extracted features and the few labelled samples are applied to train the ResNet18 model to obtain excellent classification results. The milling TCM experiments show that the classification precision of the proposed GAF-CL model is above 95% with small labelled samples, which is more than 19% higher than the ImageNet pre-training model.
准确的刀具状态监测是保证铣削质量的重要环节。然而,由于中医实验的成本,训练集中有标记的样本很少,而未标记的样本很多,这严重影响了许多机器学习模型的准确性。提出了一种基于比较学习(CL)和Gramian角场(GAF)的新方法来提高TCM的性能。TCM实验中采集的所有样品(包括有标记和未标记的样品)的每个通道的切割力信号通过GAF扩展为灰度图像,并与其他通道合并为彩色图像。然后,将这些彩色图像输入到CL预训练模型中学习特征。最后,将提取的特征和少量标记的样本应用于ResNet18模型的训练,获得优异的分类效果。铣削中医实验表明,在标记样本较小的情况下,本文提出的GAF-CL模型的分类精度在95%以上,比ImageNet预训练模型提高了19%以上。
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引用次数: 4
Methods to improve motion servo control accuracy of pneumatic cylinders - review and prospect 提高气缸运动伺服控制精度的方法——综述与展望
Q1 Engineering Pub Date : 2023-01-01 DOI: 10.1504/ijhm.2023.132301
Pengfei Qian, Lei Liu, Chenwei Pu, Deyuan Meng, Luis Miguel Ruiz Páez
Low-cost, non-polluting pneumatic technology is one of the most important engineering technologies. However, the application of pneumatic servo system is limited by the low control accuracy due to the nonlinear factors such as pneumatic actuator friction and compressibility of working medium. Many researchers have been exploring the reasons for this and have taken some targeted measures. Through extensive literature research, this paper summarises these methods into four categories: high-precision friction modelling and compensation, advanced control strategies, improved system stiffness and improved friction characteristics. In addition, the paper offers a new idea that removes the uncertain part of friction that is harmful to the control system and retains the damping part that is beneficial to the control system. For example, it can be considered to introduce a deterministic damping coefficient after removing the friction to enhance the stability of the system, and thus improve the control accuracy of the pneumatic system.
低成本、无污染的气动技术是最重要的工程技术之一。然而,由于气动执行器的摩擦和工作介质的可压缩性等非线性因素,控制精度较低,限制了气动伺服系统的应用。许多研究者一直在探索其原因,并采取了一些有针对性的措施。通过大量的文献研究,本文将这些方法归纳为四类:高精度的摩擦建模与补偿、先进的控制策略、改善系统刚度和改善摩擦特性。此外,本文还提出了一种新的思想,即去除对控制系统有害的摩擦不确定部分,保留对控制系统有益的阻尼部分。例如,可以考虑在去除摩擦后引入确定性阻尼系数来增强系统的稳定性,从而提高气动系统的控制精度。
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引用次数: 2
A comparative study of energy-efficient clustering protocols for WSN-internet-of-things 无线网络-物联网节能聚类协议比较研究
IF 5.1 Q1 Engineering Pub Date : 2023-01-01 DOI: 10.1504/ijhm.2023.10055521
S. Umarani, T. Kanimozhi
{"title":"A comparative study of energy-efficient clustering protocols for WSN-internet-of-things","authors":"S. Umarani, T. Kanimozhi","doi":"10.1504/ijhm.2023.10055521","DOIUrl":"https://doi.org/10.1504/ijhm.2023.10055521","url":null,"abstract":"","PeriodicalId":29937,"journal":{"name":"International Journal of Hydromechatronics","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66895052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An improved gated convolutional neural network for rolling bearing fault diagnosis with imbalanced data 基于改进门控卷积神经网络的不平衡滚动轴承故障诊断
IF 5.1 Q1 Engineering Pub Date : 2023-01-01 DOI: 10.1504/ijhm.2023.10055520
Guojin Feng, D. Zhen, Shaoning Tian, Rahizar Bin Ramli, Xiaoxia Liang, Jie Yang, Changsheng Xi
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引用次数: 4
Prescribed performance control for a pneumatic cylinder with strong friction via nonlinear extended state observer 基于非线性扩展状态观测器的强摩擦气缸预定性能控制
IF 5.1 Q1 Engineering Pub Date : 2023-01-01 DOI: 10.1504/ijhm.2023.10058748
Bo Liu, Ling Zhao, Ce Yan, Xin Liu, Shaomeng Gu
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引用次数: 0
A machine learning-based image classification of silicon solar cells 基于机器学习的硅太阳能电池图像分类
Q1 Engineering Pub Date : 2023-01-01 DOI: 10.1504/ijhm.2023.10060268
P.R. Budarapu, S.D.V.S.S. Varma Siruvuri, H. Verma
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引用次数: 0
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International Journal of Hydromechatronics
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