Measurement Science and Technology最新文献_第4页

A bearing fault diagnosis method with improved symplectic geometry mode decomposition and feature selection 改进的交映体几何模式分解和特征选择轴承故障诊断方法

IF 2.4 3区工程技术

Measurement Science and Technology Pub Date : 2024-01-05 DOI: 10.1088/1361-6501/ad1ba4

Shengfan Chen, Xiaoxia Zheng

{"title":"A bearing fault diagnosis method with improved symplectic geometry mode decomposition and feature selection","authors":"Shengfan Chen, Xiaoxia Zheng","doi":"10.1088/1361-6501/ad1ba4","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1ba4","url":null,"abstract":"\u0000 A rolling bearing fault diagnosis method based on improved symplectic geometry mode decomposition and feature selection was proposed to solve the problem of low fault identification due to the influence of noise on early bearing fault features. First, the symplectic geometry mode decomposition is improved to enhance its robustness in decomposing signals with noise, then the time domain, frequency domain, and time-frequency features of each symplectic geometric component are extracted as feature vectors. Second, a comprehensive feature selection strategy is proposed to select the optimal subset of features that are conducive to fault classification. Finally, considering the problem of low classification accuracy of a single machine learning model, the AdaBoost-WSO-SVM model is constructed for fault classification using the AdaBoost algorithm of integrated learning. Experimental decomposition of complex signals with noise indicates that the improved symplectic geometry mode decomposition is more effective compared to traditional symplectic geometry mode decomposition. Subsequently, multiple experiments were conducted using the bearing datasets from Case Western Reserve University (CWRU) and Jiangnan University (JNU). The experimental results reveal that, after comprehensive feature selection and ensemble learning pattern recognition experiments on the CWRU dataset, the average accuracy of fault diagnosis can reach 99.67%. On the JNU dataset, the proposed fault diagnosis method achieves an average accuracy of 95.03%. This suggests that, compared to other feature selection methods and classification models, the proposed approach in this paper exhibits higher accuracy and generalization capabilities.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"106 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Curve-Based Segmentation Method for Point Clouds of Curved and Irregular Structures 基于曲线的曲面和不规则结构点云增强型分割方法

IF 2.4 3区工程技术

Measurement Science and Technology Pub Date : 2024-01-05 DOI: 10.1088/1361-6501/ad1ba1

Limei Song, Zongyang Zhang, Chongdi Xu, Yangang Yang, Xinjun Zhu

{"title":"Enhanced Curve-Based Segmentation Method for Point Clouds of Curved and Irregular Structures","authors":"Limei Song, Zongyang Zhang, Chongdi Xu, Yangang Yang, Xinjun Zhu","doi":"10.1088/1361-6501/ad1ba1","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1ba1","url":null,"abstract":"\u0000 This paper proposes an improved method for model-based segmentation of curved and irregular mounded structures in 3D measurements. The proposed method divides the point cloud data into several levels according to the reasonable width calculated from the density of points, and then fits a curve model with 2D points to each level separately. The classification results of specific types are merged to obtain specific structural measurement data in 3D space. Experiments were conducted on the proposed method using the region growth algorithm (SRG) and the model-based segmentation method (MS) provided in the PCL library as the control group. The results show that the proposed method achieves higher accuracy with a mean intersection merge ratio (MloU) of more than 0.8238, which is at least 37.92% higher than SRG and MS. The proposed method is also faster with a time-consuming only 1/5 of SRG and 1/2 of MS. Therefore, the proposed method is an effective and efficient way to segment the measurement data of curved and irregular mounded structures in 3D measurements. The method proposed in this paper has also applied in the practical robotic grinding task, the root mean square error of the grinding amount is less than 2 mm, and good grinding results are achieved.grinding results are achieved.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"89 7","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nitriding layer depth detection based on mixing frequency nonlinear ultrasonic parameters 基于混频非线性超声参数的氮化层深度检测

IF 2.4 3区工程技术

Measurement Science and Technology Pub Date : 2024-01-05 DOI: 10.1088/1361-6501/ad1ba5

Xinxin Li, Yiwen Bi, Weili Tang, H. Mao, Zhenfeng Huang

{"title":"Nitriding layer depth detection based on mixing frequency nonlinear ultrasonic parameters","authors":"Xinxin Li, Yiwen Bi, Weili Tang, H. Mao, Zhenfeng Huang","doi":"10.1088/1361-6501/ad1ba5","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1ba5","url":null,"abstract":"\u0000 Nitriding treatment can improve the surface properties of workpieces, thus increasing the service life of the workpiece. The depth of nitriding layer is not only one of the important indexes for evaluating the nitriding effect, but also an important factor affecting the end-use performance of the workpiece. While the existing hardness and metallographic methods cannot meet the needs for non-destructive testing of nitriding layer depth in shaft parts. Therefore, a method using non-linear ultrasonic testing technology is proposed for non-destructive evaluation of nitriding layer depth. In this study, 1045 steel shaft specimens with different nitriding layer depths were prepared by a liquid salt bath nitriding method. The total depth of the nitriding layer was measured using a microhardness tester, and metallographic microscopy was applied to observe microstructure changes before and after nitriding treatment. With the proposed non-destructive method, the longitudinal critically refracted (LCR) wave mixing detection model was established and the ultrasonic nonlinear coefficients were used for characterizing the nitrided layer depths. Experimental results show that the LCR wave sum frequency (LCRWSF) detection model of ultrasonic nonlinear coefficient is better to characterize the nitriding layer depth of 1045 steel and have higher sensitivity. As a result, the LCRWSF model is more suitable to efficiently estimate the nitrided layer depth.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"103 11","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and dynamic analysis of a highly sensitive MEMS gyroscope based on mode localization. 基于模式定位的高灵敏度 MEMS 陀螺仪的设计与动态分析

IF 2.4 3区工程技术

Measurement Science and Technology Pub Date : 2024-01-05 DOI: 10.1088/1361-6501/ad1ba6

Wei Hou, qichang zhang, Shu ying Hao, Kunpeng Zhang

{"title":"Design and dynamic analysis of a highly sensitive MEMS gyroscope based on mode localization.","authors":"Wei Hou, qichang zhang, Shu ying Hao, Kunpeng Zhang","doi":"10.1088/1361-6501/ad1ba6","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1ba6","url":null,"abstract":"\u0000 Micro-electromechanical systems (MEMS) gyroscope has important applications in many fields such as aviation, spaceflight, weaponry and automatic driving. To improve the robustness and sensitivity, we design a novel dual-mass MEMS gyroscope based on the mode localization in this paper. The gyroscope structure consists of a pair of perturbation systems connected with weakly coupled resonator systems (WCRS). It has the advantage of eliminating the mode matching and achieving the mode localization effect. The dynamic behaviors of MEMS gyroscope are developed by the multi-scale method. The detection characteristics of amplitude ratio (AR) and amplitude difference (AD) are compared. Combining numerical simulation, we analyzed the influence of critical parameter. It is indicated that the sensitivity can reach up to 56199.78 ppm/°/s through AR output, which is two magnitudes higher than the traditional MEMS gyroscope. For the detection of micro-angular rate, the AD output has advantages in sensitivity, and AR output has a smaller nonlinearity error. In addition, structural parameters, especially the voltage of perturbation parallel plate, have a significant impact on system sensitivity. If the breakdown voltage meets condition, the sensitivity can be enhanced more than ten times by amplifying the voltage, which further broaden the application field of the MEMS gyroscope.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"6 10","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inter-turn short circuit and demagnetization fault diagnosis of ship PMSM based on multiscale residual dilated CNN and BiLSTM 基于多尺度残差扩张 CNN 和 BiLSTM 的船舶 PMSM匝间短路和退磁故障诊断

IF 2.4 3区工程技术

Measurement Science and Technology Pub Date : 2024-01-05 DOI: 10.1088/1361-6501/ad19c0

Guo Yan, Yihuai Hu

{"title":"Inter-turn short circuit and demagnetization fault diagnosis of ship PMSM based on multiscale residual dilated CNN and BiLSTM","authors":"Guo Yan, Yihuai Hu","doi":"10.1088/1361-6501/ad19c0","DOIUrl":"https://doi.org/10.1088/1361-6501/ad19c0","url":null,"abstract":"Inter-turn short circuit (ITSC) and demagnetization of permanent magnet synchronous motors (PMSMs) can lead to serious ship accidents, timely and accurate fault diagnosis of these faults is very important. A multi-signal fusion fault diagnosis method (MD-CNN-BiLSTM) is proposed based on multi-scale residual dilated convolutional neural network (D-CNN) and bidirectional long and short-term memory (BiLSTM) for PMSM fault diagnosis. This method first takes three-phase current and vibration signals as input; uses a three-column parallel CNN structure with different scales to extract both global signal and local feature. A residual connection in the expanded CNN is then used to eliminate the problems of gradient disappearance or explosion; and finally, BiLSTM is used to further extract features and identify the fault. A 2.2 kW permanent magnet synchronous motor was used to build a fault simulation test rig. The motor stator was rewound to simulate the ITSC fault, and different sizes of permanent magnets were replaced to simulate demagnetization fault. ITSC, demagnetization and their coupled faults were simulated under 10 specific motor speeds and loads respectively. The test proved that the diagnostic accuracy of the proposed method was 4.2% higher than that of ordinary CNN and 29.06% higher than that of BiLSTM. It also had the best diagnostic effect under the noise interference of different intensities. It was verified that the proposed method has good noise interference and strong classification ability.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"11 11","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent intrusion detection for optical fiber perimeter security system based on an improved high efficiency feature extraction technique 基于改进型高效特征提取技术的光纤周界安全系统智能入侵检测

IF 2.4 3区工程技术

Measurement Science and Technology Pub Date : 2024-01-05 DOI: 10.1088/1361-6501/ad1b9f

Zhenshi Sun, Zheng Guo

{"title":"Intelligent intrusion detection for optical fiber perimeter security system based on an improved high efficiency feature extraction technique","authors":"Zhenshi Sun, Zheng Guo","doi":"10.1088/1361-6501/ad1b9f","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1b9f","url":null,"abstract":"\u0000 The automated analysis of optical fiber vibration sensing data has been highly demanded in engineering applications. Therefore, intrusion analysis, which aims at detecting, recognizing, and classifying intrusions, holds great importance for optical fiber vibration sensing. In this work, an intelligent intrusion detection scheme employing an improved high-efficiency feature extraction technique and utilizing a dual Mach-Zehnder interferometer (DMZI)-based optical fiber perimeter security system is proposed. So, the DMZI-based perimeter security system in practical settings can be successfully established. Specifically, time-frequency feature vectors with nine features are firstly constructed using a maximal overlap discrete wavelet transformation approach and a zero crossing rate method. Then, the feature vectors are classified into corresponding categories using a radial basis function neural network. The effectiveness of the proposed scheme has been validated using six types of human intrusions, such as knocking, climbing, waggling, cutting, crashing and kicking the fence. The results show that the given intrusions can be accurately and rapidly recognized by the proposed scheme. The average recognition rate of 95.0% is achieved, and the average processing time for each sample data is only 0.033 s, which is significantly lower than the sampling interval (0.3 s) in our experiment. It is believed that the proposed scheme holds promising potential in the field of optical fiber perimeter security systems.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"25 6","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm 使用基于麻雀搜索算法的改良型深度极端学习机进行预测工具穿戴

IF 2.4 3区工程技术

Measurement Science and Technology Pub Date : 2024-01-05 DOI: 10.1088/1361-6501/ad1ba0

Wenjun Zhou, Xiaoping Xiao, Zisheng Li, kai Zhang, Ruide He

引用次数: 0

Incipient Fault Detection Based on Ensemble Learning and Distribution Dissimilarity Analysis in Multi-feature Processes 基于多特征过程中的集合学习和分布相似性分析的初期故障检测

IF 2.4 3区工程技术

Measurement Science and Technology Pub Date : 2024-01-05 DOI: 10.1088/1361-6501/ad1ba2

Meizhi Liu, Xiangyu Kong, Jiayu Luo, Lei Yang

{"title":"Incipient Fault Detection Based on Ensemble Learning and Distribution Dissimilarity Analysis in Multi-feature Processes","authors":"Meizhi Liu, Xiangyu Kong, Jiayu Luo, Lei Yang","doi":"10.1088/1361-6501/ad1ba2","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1ba2","url":null,"abstract":"\u0000 Timely and accurate detection of incipient faults has attracted considerable attention and research interest in recent years, due to its potential for the prevention of serious safety incidents and for supporting preventive maintenance. However, most existing methods use single detection model, neglecting the coexistence of multiple features and the local data distribution information found in industrial scenes. To overcome this problem, an incipient fault detection method named multiple model ensemble and distribution dissimilarity analysis (MME-DISSIM) is proposed. First, various multivariate statistical analysis methods are employed as basic detectors to comprehensively capture the feature information hidden in the process data. Second, distribution dissimilarity analysis is performed to evaluate the dissimilarity between the current sliding window and each training subset. This evaluation allows for the calculation of weighting factors for each basic detector, which helps to preserve the local distribution information of the current sliding window. Third, ensemble learning is utilized to integrate the statistics from all basic detectors into two detection indices to determine the operation status of the system. In addition, two measurement metrics are defined to quantitatively analyze the fault level of incipient faults. Finally, several experiments on a numerical case, Tennessee Eastman process, and actual PROcess NeTwork Optimization are presented to verify the efficacy and superiority of the proposed method.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"38 9","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive graph-guided joint soft clustering and distribution alignment for cross-load and cross-device rotating machinery fault transfer diagnosis 用于跨负载和跨设备旋转机械故障转移诊断的自适应图引导联合软聚类和分布排列

IF 2.4 3区工程技术

Measurement Science and Technology Pub Date : 2024-01-05 DOI: 10.1088/1361-6501/ad1ba3

Huoyao Xu, Xiangyu Peng, Junlang Wang, Jie Liu, Chaoming He

{"title":"Adaptive graph-guided joint soft clustering and distribution alignment for cross-load and cross-device rotating machinery fault transfer diagnosis","authors":"Huoyao Xu, Xiangyu Peng, Junlang Wang, Jie Liu, Chaoming He","doi":"10.1088/1361-6501/ad1ba3","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1ba3","url":null,"abstract":"\u0000 Domain adaptation (DA) is an effective solution for addressing the domain shift problem. However, existing DA techniques usually directly match the distributions of the data in the original feature space, where some of the features may be distorted by a large domain shift. Besides, geometric and clustering structures of the data, which play a significant role in revealing hidden failure patterns, are not considered in traditional DA methods. To tackle the above issues, a new joint soft clustering and distribution alignment with graph embedding (JSCDA-GE) method is proposed. Specifically, weighted subspace alignment (WSA) is proposed to align bases of source and target subspaces by combining instance reweighting and subspace alignment strategies. Then, JSCDA-GE formulates an objective function by incorporating dynamic distribution alignment (DDA), soft large margin clustering (SLMC), and graph embedding (GE) in a unified structural risk minimization (SRM) framework. Ultimately, JSCDA-GE aims to learn a generalization classifier for fault diagnosis. Its effectiveness and superiority have been confirmed through thirty-six tasks on two bearing databases.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"30 9","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-Based Wind noise Prediction Study for Automotive Clay Model 基于深度学习的汽车粘土模型风噪预测研究

IF 2.4 3区工程技术

Measurement Science and Technology Pub Date : 2024-01-04 DOI: 10.1088/1361-6501/ad1b34

Lina Huang, Dengfeng Wang, Xiaolin Cao, Xiaopeng Zhang, Bingtong Huang, Yang He, Gottfried Grabner

{"title":"Deep Learning-Based Wind noise Prediction Study for Automotive Clay Model","authors":"Lina Huang, Dengfeng Wang, Xiaolin Cao, Xiaopeng Zhang, Bingtong Huang, Yang He, Gottfried Grabner","doi":"10.1088/1361-6501/ad1b34","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1b34","url":null,"abstract":"\u0000 Analyzing and mitigating wind noise in automobiles under high-speed conditions is a significant issue within the realm of Noise, Vibration, and Harshness (NVH). Due to the intricate nature of aeroacoustics generation mechanisms, current conventional methods for wind noise prediction have limitations. Hence, deep learning methods are introduced to investigate wind noise in the side window area of an automotive clay model.During aeroacoustic wind tunnel experiments, side window vibration data and noise data from the driver were collected under vehicle speed conditions of 100 km/h, 120 km/h, and 140 km/h, respectively. These data samples were obtained to be used for training and validation of the wind noise model. Convolutional Neural Networks (CNN) and Long Short-Term Memory Neural Network (LSTM) algorithms were separately employed to reveal the complex nonlinear relationship between wind noise and its influencing factors, leading to the establishment of a wind noise prediction model.Simultaneously, these two deep learning methods were compared with Backpropagation Neural Networks (BPNN), Extreme Learning Machines (ELM), and Support Vector Regression (SVR) methods. Our findings revealed that the LSTM wind noise prediction model not only exhibits higher accuracy but also demonstrates superior generalization capabilities, thereby substantiating the superiority of this method.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"44 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139385897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0