Pub Date : 2024-07-17DOI: 10.1088/1361-6501/ad646f
Junxing Li, Zhihua Wang, Lijuan Shen
Degradation of rolling bearings typically consists of two stages: a stable stage (Stage I) characterized by stable fluctuations in the health indicator (HI), and a degradation stage (Stage II) where early damage leads to HI degradation, eventually reaching the failure threshold. Therefore, to achieve online RUL prediction for bearings, three aspects should be studied: 1) Degradation modeling; 2) Inter stage change point identification; 3) Online degradation state updating. Firstly, a two-stage degradation model is constructed by simultaneously considering inherent randomness, individual differences, and measurement errors. Then, a dynamic statistical process control (SPC) method is proposed to identify the change point from Stage I to Stage II. The SPC is designed to dynamically control limits based on the bearing's condition monitoring (CM) data to prevent false alarms. An adaptive incremental filtering (AIF) is proposed to update the degradation states by simultaneously considering the state increment and the dynamics of the system noise and measurement noise. The effectiveness of the proposed method is validated on 16004 bearing test data and XJTU-SY bearing data. Results show that the proposed method can accuracy identify the change point and improve the accuracy of the prediction result during stage II.
滚动轴承的退化通常包括两个阶段：以健康指标（HI）的稳定波动为特征的稳定阶段（阶段 I）和早期损坏导致 HI 退化并最终达到失效阈值的退化阶段（阶段 II）。因此，要实现轴承的在线 RUL 预测，应从三个方面进行研究：1）退化建模；2）阶段间变化点识别；3）在线退化状态更新。首先，通过同时考虑固有随机性、个体差异和测量误差，构建了两阶段退化模型。然后，提出了一种动态统计过程控制（SPC）方法来识别从阶段 I 到阶段 II 的变化点。SPC 设计用于根据轴承的状态监测 (CM) 数据动态控制限值，以防止误报。提出了一种自适应增量滤波 (AIF)，通过同时考虑状态增量以及系统噪声和测量噪声的动态变化来更新退化状态。在 16004 轴承测试数据和 XJTU-SY 轴承数据上验证了所提方法的有效性。结果表明，所提方法能准确识别变化点，并提高了第二阶段预测结果的准确性。
{"title":"A Novel RUL Prediction Method for Rolling Bearings Based on Dynamic Control Chart and Adaptive incremental filtering","authors":"Junxing Li, Zhihua Wang, Lijuan Shen","doi":"10.1088/1361-6501/ad646f","DOIUrl":"https://doi.org/10.1088/1361-6501/ad646f","url":null,"abstract":"\u0000 Degradation of rolling bearings typically consists of two stages: a stable stage (Stage I) characterized by stable fluctuations in the health indicator (HI), and a degradation stage (Stage II) where early damage leads to HI degradation, eventually reaching the failure threshold. Therefore, to achieve online RUL prediction for bearings, three aspects should be studied: 1) Degradation modeling; 2) Inter stage change point identification; 3) Online degradation state updating. Firstly, a two-stage degradation model is constructed by simultaneously considering inherent randomness, individual differences, and measurement errors. Then, a dynamic statistical process control (SPC) method is proposed to identify the change point from Stage I to Stage II. The SPC is designed to dynamically control limits based on the bearing's condition monitoring (CM) data to prevent false alarms. An adaptive incremental filtering (AIF) is proposed to update the degradation states by simultaneously considering the state increment and the dynamics of the system noise and measurement noise. The effectiveness of the proposed method is validated on 16004 bearing test data and XJTU-SY bearing data. Results show that the proposed method can accuracy identify the change point and improve the accuracy of the prediction result during stage II.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":" 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831499","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}
Pub Date : 2024-07-17DOI: 10.1088/1361-6501/ad6471
Ying Xu, Panpan Zhao, jin wang
Currently, rainfall cannot be accurately forecasted due to poor network communication at ocean. The advantage of the BeiDou Global Navigation Satellite System (BDS-3) PPP-B2b signal, which does not rely on network communication to receive data, can provide Precipitable Water Vapor (PWV) retrieval application services for the open seas in eastern China where communication system is difficult. In this study, the data from the stations in the coastal region of China are used to establish a rainfall forecasting method for monitoring the extreme weather on the sea. Firstly, the service performance of the PPP-B2b is explored. Then, based on 17 Chinese coastal stations, the PWV accuracy is evaluated. Finally, based on the analysis of the relationship between PWV and actual rainfall, a threshold rainfall forecasting method based on sliding window is constructed. The experimental results show that: the PWV accuracy varies slightly depending on the geographic location, in which the mean absolute error (MAE) in the North Sea region is the smallest of 2.1mm, the South China Sea region is the largest of 2.60mm, and the East China Sea region is in the middle of the PWV accuracy of 2.48mm; the optimal predictors of the constructed 12-h sliding-window threshold rainfall prediction method are PWV maximum of 49 mm, PWV increase of 5 mm and PWV increase rate of 1.2 mm/h. The prediction results can reach a Critical Success Index (CSI) value of more than 45%, which has high prediction accuracy and applicability to the coastal region of China in the same period.
{"title":"Precipitable Water Vapor Retrieval for Rainfall Forecasting using BDS-3 PPP-B2b Signal in the Coastal Region of China","authors":"Ying Xu, Panpan Zhao, jin wang","doi":"10.1088/1361-6501/ad6471","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6471","url":null,"abstract":"\u0000 Currently, rainfall cannot be accurately forecasted due to poor network communication at ocean. The advantage of the BeiDou Global Navigation Satellite System (BDS-3) PPP-B2b signal, which does not rely on network communication to receive data, can provide Precipitable Water Vapor (PWV) retrieval application services for the open seas in eastern China where communication system is difficult. In this study, the data from the stations in the coastal region of China are used to establish a rainfall forecasting method for monitoring the extreme weather on the sea. Firstly, the service performance of the PPP-B2b is explored. Then, based on 17 Chinese coastal stations, the PWV accuracy is evaluated. Finally, based on the analysis of the relationship between PWV and actual rainfall, a threshold rainfall forecasting method based on sliding window is constructed. The experimental results show that: the PWV accuracy varies slightly depending on the geographic location, in which the mean absolute error (MAE) in the North Sea region is the smallest of 2.1mm, the South China Sea region is the largest of 2.60mm, and the East China Sea region is in the middle of the PWV accuracy of 2.48mm; the optimal predictors of the constructed 12-h sliding-window threshold rainfall prediction method are PWV maximum of 49 mm, PWV increase of 5 mm and PWV increase rate of 1.2 mm/h. The prediction results can reach a Critical Success Index (CSI) value of more than 45%, which has high prediction accuracy and applicability to the coastal region of China in the same period.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":" 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141827949","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}
Pub Date : 2024-07-17DOI: 10.1088/1361-6501/ad646c
Chenxi Zhao, Zeliang Liu, Zihao Pan, Lei Yu
Currently, one of the key technologies for autonomous navigation of unmanned mobile robots is SLAM, which faces many challenges in practical applications. These challenges include a lack of texture, deterioration in sensor performance, and interference from moving objects in dynamic outdoor environments, all of which have an impact on the mapping system. To address these issues, this paper proposes a framework for lidar, vision camera, and inertial navigation data, resulting in fusion and dynamic object removing. The system consists of three sub-modules: the Lidar-Inertial Module (LIM), the Visual-Inertial Module (VIM), and the Dynamic-Object-Removing Module (DORM). LIM and VIM assist each other, with lidar point clouds providing three-dimensional information for the global voxel map and the camera providing pixel-level color information. At the same time, the DORM performs synchronous dynamic object detection to remove dynamic objects from the global map. The system constructs a multi-sensor factor graph using the state and observation models, and the optimal solution is obtained using least squares. Furthermore, this paper employs triangle descriptors and bundle adjustment methods for loop closure detection in order to reduce accumulated errors and maintain consistency. Experimental results demonstrate that the system can perform clean state estimation, dynamic removing and scene reconstruction in a variety of complex scenarios.
目前，无人移动机器人自主导航的关键技术之一是 SLAM，但它在实际应用中面临许多挑战。这些挑战包括缺乏纹理、传感器性能下降以及动态室外环境中移动物体的干扰，所有这些都会对绘图系统产生影响。为了解决这些问题，本文提出了一个激光雷达、视觉相机和惯性导航数据的框架，从而实现融合和动态物体移除。该系统由三个子模块组成：激光雷达-惯性模块（LIM）、视觉-惯性模块（VIM）和动态物体移除模块（DORM）。LIM 和 VIM 相互协助，激光雷达点云为全局体素图提供三维信息，摄像头提供像素级色彩信息。同时，DORM 执行同步动态物体检测，从全局地图中移除动态物体。该系统利用状态和观测模型构建多传感器因子图，并利用最小二乘法获得最优解。此外，本文还采用三角形描述符和捆绑调整方法进行闭环检测，以减少累积误差并保持一致性。实验结果表明，该系统可以在各种复杂场景中执行干净的状态估计、动态移除和场景重建。
{"title":"A Dynamic Object Removing 3D Reconstruction System Based on Multi-Sensor Fusion","authors":"Chenxi Zhao, Zeliang Liu, Zihao Pan, Lei Yu","doi":"10.1088/1361-6501/ad646c","DOIUrl":"https://doi.org/10.1088/1361-6501/ad646c","url":null,"abstract":"\u0000 Currently, one of the key technologies for autonomous navigation of unmanned mobile robots is SLAM, which faces many challenges in practical applications. These challenges include a lack of texture, deterioration in sensor performance, and interference from moving objects in dynamic outdoor environments, all of which have an impact on the mapping system. To address these issues, this paper proposes a framework for lidar, vision camera, and inertial navigation data, resulting in fusion and dynamic object removing. The system consists of three sub-modules: the Lidar-Inertial Module (LIM), the Visual-Inertial Module (VIM), and the Dynamic-Object-Removing Module (DORM). LIM and VIM assist each other, with lidar point clouds providing three-dimensional information for the global voxel map and the camera providing pixel-level color information. At the same time, the DORM performs synchronous dynamic object detection to remove dynamic objects from the global map. The system constructs a multi-sensor factor graph using the state and observation models, and the optimal solution is obtained using least squares. Furthermore, this paper employs triangle descriptors and bundle adjustment methods for loop closure detection in order to reduce accumulated errors and maintain consistency. Experimental results demonstrate that the system can perform clean state estimation, dynamic removing and scene reconstruction in a variety of complex scenarios.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828513","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}
Traction motor bearings are crucial for guaranteeing the safe operation of metro vehicles. However, in the metro traction motor bearing fault diagnosis, there are usually problems of small samples and missing fault samples, leading to inaccurate results. Therefore, a novel bearing fault diagnosis method utilizing a track-vehicle-bearing coupled dynamic model and the IDCG-MAMCNN model is proposed in this paper. The IDCG-MAMCNN model combines an improved deep convolutional generative adversarial network (IDCGAN) with a multi-scale convolutional neural network with mixed attention (MA-MCNN). Specifically, simulation data is first provided by the coupled dynamic model to supplement missing fault samples. Secondly, the IDCGAN, along with a training method that involves pre-training models with simulation samples and fine-tuning models with experimental samples, is introduced to generate high-quality samples and augment experimental samples under small samples. Lastly, the MA-MCNN serves as the classification model, trained with the augmented dataset comprising experimental, simulation, and generated samples. The fault diagnosis performance of the proposed method is evaluated on the experimental samples of two bearing datasets under small samples and various conditions of missing fault samples. It has been demonstrated by the experimental results that the proposed method exhibits robust fault diagnosis performance and generates high-quality samples under small samples and missing fault samples. Furthermore, the proposed method showcases its adaptability to different operation speeds.
{"title":"Simulation data-driven fault diagnosis method for metro traction motor bearings under small samples and missing fault samples","authors":"Kailin Bi, Aihua Liao, Dingyu Hu, Wei Shi, Rongming Liu, Changjiang Sun","doi":"10.1088/1361-6501/ad6470","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6470","url":null,"abstract":"\u0000 Traction motor bearings are crucial for guaranteeing the safe operation of metro vehicles. However, in the metro traction motor bearing fault diagnosis, there are usually problems of small samples and missing fault samples, leading to inaccurate results. Therefore, a novel bearing fault diagnosis method utilizing a track-vehicle-bearing coupled dynamic model and the IDCG-MAMCNN model is proposed in this paper. The IDCG-MAMCNN model combines an improved deep convolutional generative adversarial network (IDCGAN) with a multi-scale convolutional neural network with mixed attention (MA-MCNN). Specifically, simulation data is first provided by the coupled dynamic model to supplement missing fault samples. Secondly, the IDCGAN, along with a training method that involves pre-training models with simulation samples and fine-tuning models with experimental samples, is introduced to generate high-quality samples and augment experimental samples under small samples. Lastly, the MA-MCNN serves as the classification model, trained with the augmented dataset comprising experimental, simulation, and generated samples. The fault diagnosis performance of the proposed method is evaluated on the experimental samples of two bearing datasets under small samples and various conditions of missing fault samples. It has been demonstrated by the experimental results that the proposed method exhibits robust fault diagnosis performance and generates high-quality samples under small samples and missing fault samples. Furthermore, the proposed method showcases its adaptability to different operation speeds.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828844","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}
Big data-driven rotating machine intelligent diagnostic technology has gained widespread applications. In practice, however, fault data are limited as well as inconsistencies in fault categories among different domains are widespread. These make developing robust intelligent diagnostic models a challenge. To this end, this paper develops an enhanced meta-learning network with a sensitivity penalization mechanism (EMLN-SP) for few-shot fault diagnosis in severe domain bias. First, lightweight channel attention is introduced to establish an enhanced feature encoder under meta-learning framework, which elevates the key feature expression to facilitate the extraction of generalized diagnostic knowledge within limited samples. Second, a boundary-enhanced loss calculation method is designed, which boosts the focus for decision boundary information to prevent the model from the overfitting dilemma in the case of few-shot. Finally, a sensitivity penalty mechanism is constructed to adjust the optimization direction, which prevents the model from falling into a local optimum, to boost the generalization of the model performance. The effectiveness of EMLN-SP is validated by three cross-domain diagnostic cases with diverse domain offsets.
{"title":"An enhanced meta-learning network with sensitivity penalty for cross-domain few-shot fault diagnosis","authors":"Hongkai Jiang, Mingzhe Mu, Wenxin Jiang, Yutong Dong, Zhenghong Wu","doi":"10.1088/1361-6501/ad5039","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5039","url":null,"abstract":"\u0000 Big data-driven rotating machine intelligent diagnostic technology has gained widespread applications. In practice, however, fault data are limited as well as inconsistencies in fault categories among different domains are widespread. These make developing robust intelligent diagnostic models a challenge. To this end, this paper develops an enhanced meta-learning network with a sensitivity penalization mechanism (EMLN-SP) for few-shot fault diagnosis in severe domain bias. First, lightweight channel attention is introduced to establish an enhanced feature encoder under meta-learning framework, which elevates the key feature expression to facilitate the extraction of generalized diagnostic knowledge within limited samples. Second, a boundary-enhanced loss calculation method is designed, which boosts the focus for decision boundary information to prevent the model from the overfitting dilemma in the case of few-shot. Finally, a sensitivity penalty mechanism is constructed to adjust the optimization direction, which prevents the model from falling into a local optimum, to boost the generalization of the model performance. The effectiveness of EMLN-SP is validated by three cross-domain diagnostic cases with diverse domain offsets.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"53 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102076","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}
Aiming at the problem that the dynamic balance process of a flexible rotor needs to start and stop frequently to add test weight, which is time-consuming and labor-consuming, and the balance accuracy is difficult to guarantee, a dynamic balance optimization method of flexible rotor based on Grey Wolf Optimizer(GWO) is proposed. In this paper, a virtual prototype model is established based on a power turbine rotor for a certain type of turboshaft engine, and a rotor test platform is built. The transfer function is used to find the relationship between unbalance and vibration response, and the equilibrium equations are established to solve the problem. In the process of solving the problem that the equilibrium equations are mostly contradictory, GWO is used to solve the contradictory equations to obtain the optimal counterweight scheme at the full working speed of the rotor. The results show that the method proposed in this paper eliminates the tedious test weight process of traditional dynamic balance, and the vibration reduction effect is better than the conventional on-site dynamic balance. The work of this paper can improve the efficiency and accuracy of flexible rotor dynamic balance and provide technical reference for the vibration control of aero-engine.
{"title":"Research on dynamic balance optimization method of flexible rotor based on GWO","authors":"Fanyu Zhang, Xuejun LI, Qingkai Han, Shuaiping Guo, Shuo Han, Hongxian Zhang","doi":"10.1088/1361-6501/ad5036","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5036","url":null,"abstract":"\u0000 Aiming at the problem that the dynamic balance process of a flexible rotor needs to start and stop frequently to add test weight, which is time-consuming and labor-consuming, and the balance accuracy is difficult to guarantee, a dynamic balance optimization method of flexible rotor based on Grey Wolf Optimizer(GWO) is proposed. In this paper, a virtual prototype model is established based on a power turbine rotor for a certain type of turboshaft engine, and a rotor test platform is built. The transfer function is used to find the relationship between unbalance and vibration response, and the equilibrium equations are established to solve the problem. In the process of solving the problem that the equilibrium equations are mostly contradictory, GWO is used to solve the contradictory equations to obtain the optimal counterweight scheme at the full working speed of the rotor. The results show that the method proposed in this paper eliminates the tedious test weight process of traditional dynamic balance, and the vibration reduction effect is better than the conventional on-site dynamic balance. The work of this paper can improve the efficiency and accuracy of flexible rotor dynamic balance and provide technical reference for the vibration control of aero-engine.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"5 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100373","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}
Pub Date : 2024-05-24DOI: 10.1088/1361-6501/ad5037
若愚 李, Yanqiu Pan, Qi Fan, Wei Wang, Ruling Ren
In modern industrial systems, bearing failures account for 30–40% of industrial machinery faults. Traditional convolutional neural network suffers from gradient vanishing and overfitting, resulting in a poor diagnostic accuracy. To address the issues, a new bearing fault diagnosis approach was proposed based on an improved AlexNet neural network combined with transfer learning. After decomposition and noise-reduction, reconstructed vibration signals were transformed into 2D images, then input into the improved AlexNet for training and follow-up transfer learning. Program auto-tuning and image-enhancing techniques were employed to increase the diagnostic accuracy in this study. The approach was verified with the datasets from Case Western Reserve University (CWRU), Jiangnan University (JNU), and the Association for Mechanical Failure Prevention Technology (MFPT). The results showed that the diagnostic accuracies by normal learning were more than 97% for CWRU and JNU datasets, and 100% for MFPT dataset. After transfer learning, the accuracies all reached above 99.5%. The proposed approach was demonstrated to be able to effectively diagnose the bearing faults.
{"title":"A bearing fault diagnosis approach based on an improved neural network combined with transfer learning","authors":"若愚 李, Yanqiu Pan, Qi Fan, Wei Wang, Ruling Ren","doi":"10.1088/1361-6501/ad5037","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5037","url":null,"abstract":"\u0000 In modern industrial systems, bearing failures account for 30–40% of industrial machinery faults. Traditional convolutional neural network suffers from gradient vanishing and overfitting, resulting in a poor diagnostic accuracy. To address the issues, a new bearing fault diagnosis approach was proposed based on an improved AlexNet neural network combined with transfer learning. After decomposition and noise-reduction, reconstructed vibration signals were transformed into 2D images, then input into the improved AlexNet for training and follow-up transfer learning. Program auto-tuning and image-enhancing techniques were employed to increase the diagnostic accuracy in this study. The approach was verified with the datasets from Case Western Reserve University (CWRU), Jiangnan University (JNU), and the Association for Mechanical Failure Prevention Technology (MFPT). The results showed that the diagnostic accuracies by normal learning were more than 97% for CWRU and JNU datasets, and 100% for MFPT dataset. After transfer learning, the accuracies all reached above 99.5%. The proposed approach was demonstrated to be able to effectively diagnose the bearing faults.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099729","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}
Pub Date : 2024-05-24DOI: 10.1088/1361-6501/ad503a
Jian Wei, Shaojie Ma, Huifa Shi
In this study, a high-impact dynamic loading device was designed to generate a three-dimensional pulse excitation signal with high intensity shock acceleration and achieve triaxial synchronous calibration of a triaxial acceleration sensor. A light-gas gun interior ballistic model and a sensor mechanical response model were developed, and the relationships between the bullet impact velocity, barrel length, and initial chamber pressure were obtained. Additionally, the transformation relationship of the sensor’s triaxial acceleration in different coordinate systems was derived. Based on the stress wave theory and the finite element method, the influence of the bullet impact velocity on the variation pulse, and different slope and deflection angles on the triaxial acceleration were analyzed. By optimizing the parameter design, machining the prototype, and conducting high-impact dynamic loading tests, the results showed that the deviation between theoretical and measured values of the generated triaxial acceleration signal was small, and the maximum deviation was less than 4%. This indicated that the proposed high-impact dynamic loading device satisfied the calibration requirements for calibrating triaxial acceleration sensors, which can generate a three-dimensional acceleration with a peak value of not less than 700,000 m/s².
{"title":"High-Impact Dynamic Loading Method for Calibration of Triaxial Acceleration Sensors","authors":"Jian Wei, Shaojie Ma, Huifa Shi","doi":"10.1088/1361-6501/ad503a","DOIUrl":"https://doi.org/10.1088/1361-6501/ad503a","url":null,"abstract":"\u0000 In this study, a high-impact dynamic loading device was designed to generate a three-dimensional pulse excitation signal with high intensity shock acceleration and achieve triaxial synchronous calibration of a triaxial acceleration sensor. A light-gas gun interior ballistic model and a sensor mechanical response model were developed, and the relationships between the bullet impact velocity, barrel length, and initial chamber pressure were obtained. Additionally, the transformation relationship of the sensor’s triaxial acceleration in different coordinate systems was derived. Based on the stress wave theory and the finite element method, the influence of the bullet impact velocity on the variation pulse, and different slope and deflection angles on the triaxial acceleration were analyzed. By optimizing the parameter design, machining the prototype, and conducting high-impact dynamic loading tests, the results showed that the deviation between theoretical and measured values of the generated triaxial acceleration signal was small, and the maximum deviation was less than 4%. This indicated that the proposed high-impact dynamic loading device satisfied the calibration requirements for calibrating triaxial acceleration sensors, which can generate a three-dimensional acceleration with a peak value of not less than 700,000 m/s².","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100388","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}
Pub Date : 2024-05-24DOI: 10.1088/1361-6501/ad5038
Qi Wang, Qitong Chen, Liang Chen, Changqing Shen
Cross-domain fault diagnosis is crucial for industrial applications with various and unknown operating conditions. However, due to the significant differences in the distribution of features in multiple source domains, it may lead to mutual interference of features between different domains and reduce the accuracy of diagnosis, which is a problem not considered by most current researches. In addition, most of the existing methods focus only on the extraction of low-frequency global information and cannot adequately deal with high-frequency local information. Consequently, this paper provides a multi-stage processing integrated dual-weight attention-based multi-source multi-stage aligned domain adaptation (DAMMADA) method. Global fault features that are shared by various subdomains are extracted by three domain-specific feature extractors from various domains. In a local feature extractor, the dual-weight attention module not only uses shared weights to aggregate local information, but it also uses contextual weights to improve local features. In terms of loss handling, multiple pseudo-labels are used to reduce the loss of the local maximum mean discrepancy (LMMD) in order to learn the domain-invariant characteristics after improving the high-frequency and low-frequency information extraction. To modify the classification boundaries, the pseudo-labels' mean square errors (MSE) are combined. Comprehensive experiments were carried out on two platforms for fault diagnosis of SCARA robots and bearings respectively, and the results demonstrated that DAMMADA is superior to other methods in terms of accuracy and its ability to suppress negative migration for cross-domain tasks.
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Pub Date : 2024-05-24DOI: 10.1088/1361-6501/ad5035
Ping Yang, Haiyang Lin, Runxi Wu, Shuonan Xiao
In the application of vision measurement, the black light-absorbing object is difficult to reflect the structured light from infrared emitter of the RGB-D camera. Therefore, an image recognition algorithm based on reference environment information is proposed to acquire the spatial positioning information of black volutes in the depalletizing system. The hardware system of the depalletizing system is mainly constructed of an upper computer, a six-axis industrial robot, an RGB-D camera and an end adsorption device. Firstly, the horizontal position information of each volute placed on the cardboard is obtained by the depth differences between the cardboard and the volute. Then, the depth information of the volute is obtained by the upper cardboard depth through collecting the position of the end vacuum suction cup triggered by feedback signal from vacuum generator. Secondly, a regional planar hand-eye calibration method is developed to improve the calibration accuracy in two-dimensional coordinates. The regional calibration method divides the robot working area into four regions: upper left, lower left, upper right, and lower right. The transformation matrix of each region is calculated separately. Finally, the depalletizing experiment is conducted on the three types of volutes. It is concluded that the average positioning error of the grasping center point of each volute obtained by our method is 3.795 mm, and its standard deviation is 1.769 mm. The average value of regional planar hand-eye calibration error is 4.044 mm, and its standard deviation is 1.501 mm. Under a stack of materials with dimensions of 1350 mm × 1350 mm × 1500 mm, the maximum error is controlled within 15mm. Additionally, when combined with the end feedback compensation mechanism, the success rate for grasping all three volutes reaches 100%.
在视觉测量应用中，黑色吸光物体很难反射 RGB-D 摄像机红外发射器发出的结构光。因此，提出了一种基于参考环境信息的图像识别算法，以获取卸垛系统中黑色涡卷的空间定位信息。卸垛系统的硬件系统主要由上位机、六轴工业机器人、RGB-D 摄像机和末端吸附装置组成。首先，通过纸板与涡卷之间的深度差，获得放置在纸板上的每个涡卷的水平位置信息。然后，通过收集真空发生器反馈信号触发的末端真空吸盘的位置，根据纸板上部深度获得涡卷的深度信息。其次，开发了一种区域平面手眼校准方法，以提高二维坐标的校准精度。区域校准法将机器人工作区域划分为左上、左下、右上和右下四个区域。每个区域的变换矩阵分别计算。最后，对三种类型的涡卷进行了去垛实验。结果表明，用我们的方法得到的每个涡卷的抓取中心点的平均定位误差为 3.795 毫米，标准偏差为 1.769 毫米。区域平面手眼校准误差的平均值为 4.044 毫米，标准偏差为 1.501 毫米。在堆放尺寸为 1350 mm × 1350 mm × 1500 mm 的材料时，最大误差控制在 15 mm 以内。此外，结合末端反馈补偿机制，抓取所有三个涡卷的成功率达到 100%。
{"title":"A new novel recognition and positioning system of black light-absorbing volute for automation depalletizing development","authors":"Ping Yang, Haiyang Lin, Runxi Wu, Shuonan Xiao","doi":"10.1088/1361-6501/ad5035","DOIUrl":"https://doi.org/10.1088/1361-6501/ad5035","url":null,"abstract":"\u0000 In the application of vision measurement, the black light-absorbing object is difficult to reflect the structured light from infrared emitter of the RGB-D camera. Therefore, an image recognition algorithm based on reference environment information is proposed to acquire the spatial positioning information of black volutes in the depalletizing system. The hardware system of the depalletizing system is mainly constructed of an upper computer, a six-axis industrial robot, an RGB-D camera and an end adsorption device. Firstly, the horizontal position information of each volute placed on the cardboard is obtained by the depth differences between the cardboard and the volute. Then, the depth information of the volute is obtained by the upper cardboard depth through collecting the position of the end vacuum suction cup triggered by feedback signal from vacuum generator. Secondly, a regional planar hand-eye calibration method is developed to improve the calibration accuracy in two-dimensional coordinates. The regional calibration method divides the robot working area into four regions: upper left, lower left, upper right, and lower right. The transformation matrix of each region is calculated separately. Finally, the depalletizing experiment is conducted on the three types of volutes. It is concluded that the average positioning error of the grasping center point of each volute obtained by our method is 3.795 mm, and its standard deviation is 1.769 mm. The average value of regional planar hand-eye calibration error is 4.044 mm, and its standard deviation is 1.501 mm. Under a stack of materials with dimensions of 1350 mm × 1350 mm × 1500 mm, the maximum error is controlled within 15mm. Additionally, when combined with the end feedback compensation mechanism, the success rate for grasping all three volutes reaches 100%.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"9 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100831","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}
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