Pub Date : 2023-05-01DOI: 10.1109/PHM58589.2023.00020
Qingluan Guan, Xiukun Wei
Prognostics and health management (PHM) is a core technology in the domain of reliability, and it has got extensive acclamation and application. The statistical data-driven method prediction method has become a popular hotspot of research in recent years since it only considers the condition monitoring data and relevant degradation information. As one of the data-driven remaining useful life (RUL) prediction methods, the Wiener process-based method is commonly used. Considering the uncertainty existing in the degradation process for the equipment or device, this paper summarizes the statistical data-driven method and focuses on the Wiener process-based method. Finally, some urgent issues to be addressed in the future are discussed.
{"title":"The Statistical Data-driven Remaining Useful Life Prediction—A Review on the Wiener Process-based Method","authors":"Qingluan Guan, Xiukun Wei","doi":"10.1109/PHM58589.2023.00020","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00020","url":null,"abstract":"Prognostics and health management (PHM) is a core technology in the domain of reliability, and it has got extensive acclamation and application. The statistical data-driven method prediction method has become a popular hotspot of research in recent years since it only considers the condition monitoring data and relevant degradation information. As one of the data-driven remaining useful life (RUL) prediction methods, the Wiener process-based method is commonly used. Considering the uncertainty existing in the degradation process for the equipment or device, this paper summarizes the statistical data-driven method and focuses on the Wiener process-based method. Finally, some urgent issues to be addressed in the future are discussed.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126103571","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}
Remaining Useful Life (RUL) prognostics and pre-failure warning for complex industrial systems enables the timely detection of hidden problems and effectively avoids multiple accidents. Therefore, highly accurate and reliable RUL prediction is crucial. Bayesian neural networks can model the uncertainty in the process of equipment degradation while effectively assessing RUL, which helps to implement reliable risk analysis and maintenance decisions. In this paper, we propose a Convolutional Bayesian Long Short-Term Memory neural network (CB-LSTM)-based RUL prediction algorithm, which uses a Convolutional Neural Network (CNN) to implicitly extract features from training data, to generate an abstract representation of the input signal, and combine it with a Bayesian Long Short-Term Memory neural network (B-LSTM) to build a multivariate time series prediction model. The method is validated on the C-MAPSS dataset by NASA. The experimental results show that the method has good prediction accuracy and uncertainty quantification ability.
{"title":"Remaining Useful Life Prognostics and Uncertainty Quantification for Aircraft Engines Based on Convolutional Bayesian Long Short-Term Memory Neural Network","authors":"Shaowei Chen, Jiawei He, Pengfei Wen, Jing Zhang, Dengshan Huang, Shuai Zhao","doi":"10.1109/PHM58589.2023.00052","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00052","url":null,"abstract":"Remaining Useful Life (RUL) prognostics and pre-failure warning for complex industrial systems enables the timely detection of hidden problems and effectively avoids multiple accidents. Therefore, highly accurate and reliable RUL prediction is crucial. Bayesian neural networks can model the uncertainty in the process of equipment degradation while effectively assessing RUL, which helps to implement reliable risk analysis and maintenance decisions. In this paper, we propose a Convolutional Bayesian Long Short-Term Memory neural network (CB-LSTM)-based RUL prediction algorithm, which uses a Convolutional Neural Network (CNN) to implicitly extract features from training data, to generate an abstract representation of the input signal, and combine it with a Bayesian Long Short-Term Memory neural network (B-LSTM) to build a multivariate time series prediction model. The method is validated on the C-MAPSS dataset by NASA. The experimental results show that the method has good prediction accuracy and uncertainty quantification ability.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124096476","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 : 2023-05-01DOI: 10.1109/PHM58589.2023.00053
Lu Zhang, C. Delpha, D. Diallo
This work proposes a method for estimating fault severity in the presence of noise using the measured currents for a 7-phase electrical machine. The method is based on analytical models in stationary reference frames and analysis of the DC and fundamental components in the four fictitious machines. The slope of the decision function from the CUSUM algorithm, which will be noticeably different depending on the fault severity, is used to assess the performance of the fault severity estimation rapidly. The effects on the decision function’s slope of the fault severity estimation for different noise levels are evaluated. The simulation results show that even in presence of high noise levels, the decision function is an efficient fault estimation indicator. When the noise level is high, the decision function and its slope are noisier. Conversely, the decision function and its slope are less noisy when the noise level is low. The results also show that for the three fault types under study (gain fault, phase shift fault, and mean value fault), the current components of the fictitious machines in the stationary frames have distinct robustness to noise.
{"title":"Performance of Fault Severity Estimation in 7-Phase Electrical Machines under Noisy Conditions","authors":"Lu Zhang, C. Delpha, D. Diallo","doi":"10.1109/PHM58589.2023.00053","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00053","url":null,"abstract":"This work proposes a method for estimating fault severity in the presence of noise using the measured currents for a 7-phase electrical machine. The method is based on analytical models in stationary reference frames and analysis of the DC and fundamental components in the four fictitious machines. The slope of the decision function from the CUSUM algorithm, which will be noticeably different depending on the fault severity, is used to assess the performance of the fault severity estimation rapidly. The effects on the decision function’s slope of the fault severity estimation for different noise levels are evaluated. The simulation results show that even in presence of high noise levels, the decision function is an efficient fault estimation indicator. When the noise level is high, the decision function and its slope are noisier. Conversely, the decision function and its slope are less noisy when the noise level is low. The results also show that for the three fault types under study (gain fault, phase shift fault, and mean value fault), the current components of the fictitious machines in the stationary frames have distinct robustness to noise.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124743078","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 : 2023-05-01DOI: 10.1109/PHM58589.2023.00027
A. Mosallam, Fares Ben Youssef, Karolina Sobczak-Oramus, Jinlong Kang, Vikrant Gupta, Nannan Shen, L. Laval
This paper presents a novel data-driven approach for modeling degradation of the neutron generator component in logging-while-drilling tools. The study begins by identifying the incipient failure modes of the neutron generator and constructing a health indicator (HI) that serves as a quantitative measure of the component’s health status. The resulting HI can be used for additional analysis and decision-making. Then, a random forest classifier is trained to establish the relationship between the extracted HI values and the corresponding degradation level labels. The proposed method is validated using actual data collected from oil well drilling operations. The experimental results demonstrate its effectiveness in accurately classifying the health state of the neutron generator component. The study is part of a long-term project aimed at developing a digital fleet management system for drilling tools.
{"title":"Data-Driven Degradation Modeling Approach for Neutron Generators in Multifunction Logging-While-Drilling Service","authors":"A. Mosallam, Fares Ben Youssef, Karolina Sobczak-Oramus, Jinlong Kang, Vikrant Gupta, Nannan Shen, L. Laval","doi":"10.1109/PHM58589.2023.00027","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00027","url":null,"abstract":"This paper presents a novel data-driven approach for modeling degradation of the neutron generator component in logging-while-drilling tools. The study begins by identifying the incipient failure modes of the neutron generator and constructing a health indicator (HI) that serves as a quantitative measure of the component’s health status. The resulting HI can be used for additional analysis and decision-making. Then, a random forest classifier is trained to establish the relationship between the extracted HI values and the corresponding degradation level labels. The proposed method is validated using actual data collected from oil well drilling operations. The experimental results demonstrate its effectiveness in accurately classifying the health state of the neutron generator component. The study is part of a long-term project aimed at developing a digital fleet management system for drilling tools.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"456 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124322070","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 : 2023-05-01DOI: 10.1109/PHM58589.2023.00039
Yaoxiang Yu, Liang Guo, Hongli Gao
The axle-box bearing (ABB) makes crucial influence on the operation of urban rail vehicles through supporting the weight of the vehicle and load, lubricating the axle neck, and reducing friction. However, wheel-polygonal wear (WPW) can compromise the stability of the vehicle by aggravating the axle-box vibration. This work aims to study the dynamic characteristics of ABB in the presence of WPW. On one hand, a vehicle-track coupled dynamics model with ABB and flexible wheelset is established. Onsite tests are implemented to validated the effectiveness of this model, and the significance of the first flexible mode are also researched. On other hand, the study also analyzes the influence of WPW amplitude and order on ABB by inputting WPW into the model at different vehicle speeds. The results indicate that the amplitude of WPW influences the axle-box vibration amplitude, with an increase in amplitude leading to an increase in vibration amplitude. However, the influence of the order of WPW is more complex due to the existence of resonance phenomenon. The findings of this study can guide the maintenance of wheel machining and repair in urban rail vehicles, providing reference and guidance for future research in this area.
{"title":"A Study on the Effect of Wheel-polygonal Wear on Dynamic Vibration Characteristics of Urban Rail Vehicle Axle-box Bearings","authors":"Yaoxiang Yu, Liang Guo, Hongli Gao","doi":"10.1109/PHM58589.2023.00039","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00039","url":null,"abstract":"The axle-box bearing (ABB) makes crucial influence on the operation of urban rail vehicles through supporting the weight of the vehicle and load, lubricating the axle neck, and reducing friction. However, wheel-polygonal wear (WPW) can compromise the stability of the vehicle by aggravating the axle-box vibration. This work aims to study the dynamic characteristics of ABB in the presence of WPW. On one hand, a vehicle-track coupled dynamics model with ABB and flexible wheelset is established. Onsite tests are implemented to validated the effectiveness of this model, and the significance of the first flexible mode are also researched. On other hand, the study also analyzes the influence of WPW amplitude and order on ABB by inputting WPW into the model at different vehicle speeds. The results indicate that the amplitude of WPW influences the axle-box vibration amplitude, with an increase in amplitude leading to an increase in vibration amplitude. However, the influence of the order of WPW is more complex due to the existence of resonance phenomenon. The findings of this study can guide the maintenance of wheel machining and repair in urban rail vehicles, providing reference and guidance for future research in this area.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131790975","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}
Most data-driven fault diagnosis methods for analog circuits achieve good results when the data satisfies the assumption of independent and equal distribution, which is difficult to achieve in real-world scenarios. To solve this problem, a fault diagnosis method for analog circuits based on Deep Subdomain Adaptation Network is presented. By incorporating the optimization of Local Maximum Mean Discrepancy loss into the training of One-dimensional Convolutional Neural Network, this method can adaptively align the feature representation of the source and target domains without labeling in the target domain. The simulation experiments of Sallen-Key band-pass filter and four-opamp biquad high-pass filter are designed. Two groups of different component parameters are selected as the data sources of source domain and target domain, noise and random offset are added to the target domain data to simulate the actual scene. Through comparative experiments, it is verified that the analog circuit fault diagnosis method presented in this paper has steady training and high accuracy.
{"title":"A Transfer Learning Method for Fault Diagnosis of Analog Circuit Using Deep Subdomain Adaptation Network","authors":"Weizheng Chen, Xu Han, Guangquan Zhao, Xiyuan Peng","doi":"10.1109/PHM58589.2023.00056","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00056","url":null,"abstract":"Most data-driven fault diagnosis methods for analog circuits achieve good results when the data satisfies the assumption of independent and equal distribution, which is difficult to achieve in real-world scenarios. To solve this problem, a fault diagnosis method for analog circuits based on Deep Subdomain Adaptation Network is presented. By incorporating the optimization of Local Maximum Mean Discrepancy loss into the training of One-dimensional Convolutional Neural Network, this method can adaptively align the feature representation of the source and target domains without labeling in the target domain. The simulation experiments of Sallen-Key band-pass filter and four-opamp biquad high-pass filter are designed. Two groups of different component parameters are selected as the data sources of source domain and target domain, noise and random offset are added to the target domain data to simulate the actual scene. Through comparative experiments, it is verified that the analog circuit fault diagnosis method presented in this paper has steady training and high accuracy.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132978090","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}
I am honored to host the DSD/SEAA event in Verona, one of the most important cities in North-Eastern Italy. Verona is a splendid city of art, well-known through the Shakespearean tragedy of Romeo and Juliet. Roman ruins, medieval vestiges, Venetian and Austrian traces can be seen all across the city, as well as antique palaces, bridges and churches. For these reasons Verona is the fourth Italian city for the number of tourists and it is recognized as a UNESCO World Heritage Site. Close to Verona, you can also visit Lake Garda, the greatest Italian lake, impressive mountains, and lovely hills full of vineyards.
{"title":"Message from the General Chair","authors":"Mark A. Gondree","doi":"10.1109/ICPADS.2006.61","DOIUrl":"https://doi.org/10.1109/ICPADS.2006.61","url":null,"abstract":"I am honored to host the DSD/SEAA event in Verona, one of the most important cities in North-Eastern Italy. Verona is a splendid city of art, well-known through the Shakespearean tragedy of Romeo and Juliet. Roman ruins, medieval vestiges, Venetian and Austrian traces can be seen all across the city, as well as antique palaces, bridges and churches. For these reasons Verona is the fourth Italian city for the number of tourists and it is recognized as a UNESCO World Heritage Site. Close to Verona, you can also visit Lake Garda, the greatest Italian lake, impressive mountains, and lovely hills full of vineyards.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133771989","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 : 2023-05-01DOI: 10.1109/PHM58589.2023.00054
Liuxing Bai
Developing as servo drive technology is, permanent-magnet synchronous motor is gradually replacing DC motor and stepper motor and become the development direction of servo drive. Because the permanent-magnet synchronous servo system is affected by the motor parameter change, external load disturbance and other factors to obtain good performance and wide speed range of permanent magnet synchronous servo system, we must study advanced control strategy and control means, so that the adaptability and strong anti-interference ability of the system are strong. In this paper, the vector control of permanent-magnet synchronous motor is simulated in MATLAB.
{"title":"Research on the vector of permanent magnet synchronous motor based on MATLAB simulation","authors":"Liuxing Bai","doi":"10.1109/PHM58589.2023.00054","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00054","url":null,"abstract":"Developing as servo drive technology is, permanent-magnet synchronous motor is gradually replacing DC motor and stepper motor and become the development direction of servo drive. Because the permanent-magnet synchronous servo system is affected by the motor parameter change, external load disturbance and other factors to obtain good performance and wide speed range of permanent magnet synchronous servo system, we must study advanced control strategy and control means, so that the adaptability and strong anti-interference ability of the system are strong. In this paper, the vector control of permanent-magnet synchronous motor is simulated in MATLAB.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130633356","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 : 2023-05-01DOI: 10.1109/PHM58589.2023.00019
Gil-hyun Kang, Hwi-Jin Kwon, In Soo Chung, Chul-Su Kim
Most of the maintenance and training of the railway vehicle of the Korean urban railway operator was conducted in the form of document-based manuals or internet-based e-learning. This training method is inefficient due to restrictions such as time, space, and human resource operation. This study is about the development of high-definition augmented reality content for innovation in existing education and training in accordance with the recent smart maintenance transition and digitalization of maintenance. To this end, the realistic contents for maintenance and training of commuter rail vehicle air compressors that can increase immersion and realism for railway vehicle maintenance workers were developed. In addition, a questionnaire evaluation was conducted on field applicability. Rail vehicle maintenance workers can receive maintenance support by efficiently accessing work information at the workplace using mobile devices. In order to evaluate the usability of the developed air compressor maintenance augmented reality content, a usability evaluation survey was conducted on 100 college students majoring in railway vehicles. The overall average score of the 6 questionnaire items for the content was 4.12 out of 5 points, which was very good. Therefore, this content is very useful for beginners in maintenance of railway vehicles and is considered to be very effective in using it for maintenance and training of air compressors in the workplace.
{"title":"A Study on the Development of Augmented Reality Contents for Air Compressor of Railway Vehicles","authors":"Gil-hyun Kang, Hwi-Jin Kwon, In Soo Chung, Chul-Su Kim","doi":"10.1109/PHM58589.2023.00019","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00019","url":null,"abstract":"Most of the maintenance and training of the railway vehicle of the Korean urban railway operator was conducted in the form of document-based manuals or internet-based e-learning. This training method is inefficient due to restrictions such as time, space, and human resource operation. This study is about the development of high-definition augmented reality content for innovation in existing education and training in accordance with the recent smart maintenance transition and digitalization of maintenance. To this end, the realistic contents for maintenance and training of commuter rail vehicle air compressors that can increase immersion and realism for railway vehicle maintenance workers were developed. In addition, a questionnaire evaluation was conducted on field applicability. Rail vehicle maintenance workers can receive maintenance support by efficiently accessing work information at the workplace using mobile devices. In order to evaluate the usability of the developed air compressor maintenance augmented reality content, a usability evaluation survey was conducted on 100 college students majoring in railway vehicles. The overall average score of the 6 questionnaire items for the content was 4.12 out of 5 points, which was very good. Therefore, this content is very useful for beginners in maintenance of railway vehicles and is considered to be very effective in using it for maintenance and training of air compressors in the workplace.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121210037","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 : 2023-05-01DOI: 10.1109/PHM58589.2023.00013
Xiaochen Gao, Xianghua Ma
To address the problem that dynamic objects, sparse environmental features, and blurred images in smart manufacturing workshops cause the performance degradation of robotic SLAM (Simultaneous Localization and Mapping) systems, semantic information and pixel-based direct method are introduced to improve the existing vision SLAM algorithm. The objects in the environment are discriminated by the target detection technique, and the results are put into the tracking thread, and the objects with high dynamic level in the results are screened twice dynamically, static points are incorporated into the matching, and dynamic points are further processed to solve the problem of effective data loss caused by the previous direct rejection of dynamic objects. To cope with the variable environment, the input data are pre-processed by an adaptive enhancement algorithm that limits the contrast, and then the camera motion is estimated by a semi-dense direct method that is insensitive to feature missing. The evaluation results on the dynamic dataset show that the error of the improved system is significantly reduced compared with ORB-SLAM2, and the estimated trajectory fits better with the real trajectory, indicating that the localization accuracy of the system is improved, and the stability and robustness are improved.
针对智能制造车间中物体动态、环境特征稀疏、图像模糊等导致机器人SLAM (Simultaneous Localization and Mapping)系统性能下降的问题，引入语义信息和基于像素的直接方法对现有视觉SLAM算法进行改进。利用目标检测技术对环境中的目标进行判别，并将结果放入跟踪线程中，对结果中动态水平较高的目标进行二次动态筛选，将静态点纳入匹配，对动态点进行进一步处理，解决了之前直接拒绝动态目标导致的有效数据丢失问题。为了应对多变的环境，输入数据通过限制对比度的自适应增强算法进行预处理，然后通过对特征缺失不敏感的半密集直接方法估计相机运动。在动态数据集上的评估结果表明，与ORB-SLAM2相比，改进后的系统误差显著减小，估计轨迹与实际轨迹拟合更好，表明系统的定位精度得到提高，稳定性和鲁棒性得到提高。
{"title":"Robot Localization and Mapping Method in Dynamic Intelligent Manufacturing Shop Environment","authors":"Xiaochen Gao, Xianghua Ma","doi":"10.1109/PHM58589.2023.00013","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00013","url":null,"abstract":"To address the problem that dynamic objects, sparse environmental features, and blurred images in smart manufacturing workshops cause the performance degradation of robotic SLAM (Simultaneous Localization and Mapping) systems, semantic information and pixel-based direct method are introduced to improve the existing vision SLAM algorithm. The objects in the environment are discriminated by the target detection technique, and the results are put into the tracking thread, and the objects with high dynamic level in the results are screened twice dynamically, static points are incorporated into the matching, and dynamic points are further processed to solve the problem of effective data loss caused by the previous direct rejection of dynamic objects. To cope with the variable environment, the input data are pre-processed by an adaptive enhancement algorithm that limits the contrast, and then the camera motion is estimated by a semi-dense direct method that is insensitive to feature missing. The evaluation results on the dynamic dataset show that the error of the improved system is significantly reduced compared with ORB-SLAM2, and the estimated trajectory fits better with the real trajectory, indicating that the localization accuracy of the system is improved, and the stability and robustness are improved.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126738817","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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