Pub Date : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042927
Yu-Shan Liang, You-Gang Chen, Teresa Bei-Yi Shen
We present a novel heuristic approach for pulse repetition interval (PRI) modulation recognition by identifying the temporal pattern based on a symbolic radar pulse train analysis. The analysis of the symbolization of radar pulse trains is presented as a metric for the ability to identify the temporal PRI modulation characteristic. The recognition approach developed based on a time series analysis technique has to transform the radar pulse trains into a corresponding sequence of symbols. We retain temporal information from transforming the time series of pulse trains through numerical computations. The PRI pattern is obtained for real-time monitoring, and then the modulation types are identified based on characteristics. The simulation results show that the proposed algorithm can effectively recognize the PRI modulation type of radar pulse trains.
{"title":"Heuristic Approach for PRI Modulation Recognition Based on Symbolic Radar Pulse Trains Analysis","authors":"Yu-Shan Liang, You-Gang Chen, Teresa Bei-Yi Shen","doi":"10.1109/ECICE55674.2022.10042927","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042927","url":null,"abstract":"We present a novel heuristic approach for pulse repetition interval (PRI) modulation recognition by identifying the temporal pattern based on a symbolic radar pulse train analysis. The analysis of the symbolization of radar pulse trains is presented as a metric for the ability to identify the temporal PRI modulation characteristic. The recognition approach developed based on a time series analysis technique has to transform the radar pulse trains into a corresponding sequence of symbols. We retain temporal information from transforming the time series of pulse trains through numerical computations. The PRI pattern is obtained for real-time monitoring, and then the modulation types are identified based on characteristics. The simulation results show that the proposed algorithm can effectively recognize the PRI modulation type of radar pulse trains.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"375 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122858188","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042855
Hansel Ongkowijoyo, Chung-Yan Lin, N. Ruseno
Beyond Visual Line of Sight (BVLOS) operations are crucial for enabling the drone industry’s upcoming phase of UAV commercial acceleration. One of requirements to support BVLOS operations in UAV is mobile network with proper connectivity. In this study, the effect of UAV flight characteristic (altitude, attitude, and speed) to mobile network quality for UAV BVLOS operations will be analyzed. However, due to the time limitation of the publication, only preliminary results are presented. First, general technical framework including hardware, software, the data stream, and network coverage is described in the methodology. Then, a prototype UAV equips with Raspberry Pi and 4G connectivity is developed. A ground test is conducted to test functionality of the system. The preliminary result shows that the framework system functioning well in terms of transfer and receive data with the average latency 40 milliseconds. Next step, the flight test will be conducted to measure the effect of the UAV flight characteristics on the mobile network quality.
{"title":"Effect of UAV Flight Characteristics to Mobile Network Quality for UAV BVLOS Operations","authors":"Hansel Ongkowijoyo, Chung-Yan Lin, N. Ruseno","doi":"10.1109/ECICE55674.2022.10042855","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042855","url":null,"abstract":"Beyond Visual Line of Sight (BVLOS) operations are crucial for enabling the drone industry’s upcoming phase of UAV commercial acceleration. One of requirements to support BVLOS operations in UAV is mobile network with proper connectivity. In this study, the effect of UAV flight characteristic (altitude, attitude, and speed) to mobile network quality for UAV BVLOS operations will be analyzed. However, due to the time limitation of the publication, only preliminary results are presented. First, general technical framework including hardware, software, the data stream, and network coverage is described in the methodology. Then, a prototype UAV equips with Raspberry Pi and 4G connectivity is developed. A ground test is conducted to test functionality of the system. The preliminary result shows that the framework system functioning well in terms of transfer and receive data with the average latency 40 milliseconds. Next step, the flight test will be conducted to measure the effect of the UAV flight characteristics on the mobile network quality.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123276853","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042937
Ziyang Yang, Xiao Ye, Xiao‐hai Yang, Nan Pan, Guangmin Li
The loss management work is closely related to the line’s operation efficiency, the power enterprise’s economic benefits, and electricity consumption safety. However, the strange relationship between the household transformer leads to the inaccurate calculation of the line loss in the station area, thus hindering the line loss management work. Therefore, given the problems of large workload, high cost, and short timeliness of identification results in traditional manual inspection, line loss fluctuation data is used to screen abnormal users of household transformer relationships. Accurate compensation editing distance (ERP) is combined with the dynamic time warping algorithm (DTW) to calculate the similarity of the user voltage curve in the abnormal station area. The SOM clustering algorithm is used to update and identify the household transformer relationship in the abnormal station area. Finally, the correlation analysis and convolutional neural network algorithm are combined to analyze and verify the updated household transformer relationship by using the power outage correlation between the station area and users, which has a specific application value.
{"title":"ERP and DTW-based Transformer-customer Identification","authors":"Ziyang Yang, Xiao Ye, Xiao‐hai Yang, Nan Pan, Guangmin Li","doi":"10.1109/ECICE55674.2022.10042937","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042937","url":null,"abstract":"The loss management work is closely related to the line’s operation efficiency, the power enterprise’s economic benefits, and electricity consumption safety. However, the strange relationship between the household transformer leads to the inaccurate calculation of the line loss in the station area, thus hindering the line loss management work. Therefore, given the problems of large workload, high cost, and short timeliness of identification results in traditional manual inspection, line loss fluctuation data is used to screen abnormal users of household transformer relationships. Accurate compensation editing distance (ERP) is combined with the dynamic time warping algorithm (DTW) to calculate the similarity of the user voltage curve in the abnormal station area. The SOM clustering algorithm is used to update and identify the household transformer relationship in the abnormal station area. Finally, the correlation analysis and convolutional neural network algorithm are combined to analyze and verify the updated household transformer relationship by using the power outage correlation between the station area and users, which has a specific application value.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121992020","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042850
Jeih-Tsyr Chung, Qinyu Lin, Fang-Yun Hu, Bo Hu, You-Shin Lin
The automatic judgment of the object’s angle enhances the work efficiency of mechanical loading and unloading, which is necessary for the workflow of non-fixed placement. Therefore, we develop a method for judging object angles imported into various scenarios. First of all, we establish the model of each tool. Before the identification process, the proposed system improves the accuracy by adjusting the brightness and contrast. Then, the position and angle of the object are judged to transmit the result to the robotic arm for gripping. In addition, we find the best gripping point according to the boundary shape of the object to enhance the stability of the moving process so that the workpiece does not fall during the process. From experimental results, after the images are captured through the camera, we attempt to determine the object’s coordinates, angles, and clamping positions to improve the efficiency of the handling process. This design is implemented in various loading and unloading processes.
{"title":"Prediction of Machining Parameters by Vibration Signal","authors":"Jeih-Tsyr Chung, Qinyu Lin, Fang-Yun Hu, Bo Hu, You-Shin Lin","doi":"10.1109/ECICE55674.2022.10042850","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042850","url":null,"abstract":"The automatic judgment of the object’s angle enhances the work efficiency of mechanical loading and unloading, which is necessary for the workflow of non-fixed placement. Therefore, we develop a method for judging object angles imported into various scenarios. First of all, we establish the model of each tool. Before the identification process, the proposed system improves the accuracy by adjusting the brightness and contrast. Then, the position and angle of the object are judged to transmit the result to the robotic arm for gripping. In addition, we find the best gripping point according to the boundary shape of the object to enhance the stability of the moving process so that the workpiece does not fall during the process. From experimental results, after the images are captured through the camera, we attempt to determine the object’s coordinates, angles, and clamping positions to improve the efficiency of the handling process. This design is implemented in various loading and unloading processes.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124235238","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042924
Manni Hou, Ning Miao, Xinyue Bi, Xun Peng, Gang Wang, Gang Ren
Visual impairment causes many inconveniences in people’s everyday activities such as traveling, socializing, or exercising. For those who are blind, maintaining a healthy and balanced lifestyle is exceedingly difficult. For instance, it is extremely challenging to participate in team sports such as basketball or football without visually locating the ball or the other players. For players with visual impairments to locate the ball with audio feedback during football matches, a customized ball equipped with sound devices is now required. Such settings, however, necessitate extremely silent settings and are challenging to implement for training or regular play. In this research, we suggest a wearable haptic display and interface design to improve football players with visual impairments’ target and player location tasks. We describe the haptic feedback design for players’ ball tracking and the system architecture facilitated by Internet of Things technology.
{"title":"Wearable Haptic Displays Design for Visual Impaired Football","authors":"Manni Hou, Ning Miao, Xinyue Bi, Xun Peng, Gang Wang, Gang Ren","doi":"10.1109/ECICE55674.2022.10042924","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042924","url":null,"abstract":"Visual impairment causes many inconveniences in people’s everyday activities such as traveling, socializing, or exercising. For those who are blind, maintaining a healthy and balanced lifestyle is exceedingly difficult. For instance, it is extremely challenging to participate in team sports such as basketball or football without visually locating the ball or the other players. For players with visual impairments to locate the ball with audio feedback during football matches, a customized ball equipped with sound devices is now required. Such settings, however, necessitate extremely silent settings and are challenging to implement for training or regular play. In this research, we suggest a wearable haptic display and interface design to improve football players with visual impairments’ target and player location tasks. We describe the haptic feedback design for players’ ball tracking and the system architecture facilitated by Internet of Things technology.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134308373","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042917
Chuan-Pin Lu, Zheng-Yang Wu
Mushroom growth depends on the microclimate in greenhouses. The environmental control system of greenhouses cannot monitor mushroom growth. Thus, the control of microclimate is not for mushroom growth but for farmers’ feelings or experiences. To develop an intelligent system for monitoring mushroom growth, an artificial intelligence algorithm based on digital image processing was proposed in this study to automatically locate mushrooms and calculate the pileus circle. Compared to the method in the literature, the low-cost image analysis algorithm was used to calculate the pileus circle in the method. The advantage of this method was using low-cost computers or embedded systems which greatly reduces the deployment cost of intelligent image systems and the utilization rate. In the proposed method, the Bayes classifier was used to separate the target from the background to improve the accuracy of the mushroom location. Then, the image preprocessing, Hough transform for circle and self-developed circle-based region matching algorithm were used to locate the mushroom and then determine the mushroom size based on the pileus circle found. In order to verify the effectiveness of the proposed method in terms of the localization accuracy of the mushroom pileus circle, the average accuracy of the proposed method was 87.0%, which was higher than that of the traditional Circle Hough Transform method by 60.7%. Moreover, its localization stability was superior to that of Circle Hough Transform and the average running time of a single image is 2.3 s. Based on the result, the effectiveness of the proposed method meets the practical requirements of mushroom cultivation.
{"title":"Development of Artificial Intelligence Algorithm based on Digital Image Processing for Calculating Growth Rate of Mushrooms","authors":"Chuan-Pin Lu, Zheng-Yang Wu","doi":"10.1109/ECICE55674.2022.10042917","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042917","url":null,"abstract":"Mushroom growth depends on the microclimate in greenhouses. The environmental control system of greenhouses cannot monitor mushroom growth. Thus, the control of microclimate is not for mushroom growth but for farmers’ feelings or experiences. To develop an intelligent system for monitoring mushroom growth, an artificial intelligence algorithm based on digital image processing was proposed in this study to automatically locate mushrooms and calculate the pileus circle. Compared to the method in the literature, the low-cost image analysis algorithm was used to calculate the pileus circle in the method. The advantage of this method was using low-cost computers or embedded systems which greatly reduces the deployment cost of intelligent image systems and the utilization rate. In the proposed method, the Bayes classifier was used to separate the target from the background to improve the accuracy of the mushroom location. Then, the image preprocessing, Hough transform for circle and self-developed circle-based region matching algorithm were used to locate the mushroom and then determine the mushroom size based on the pileus circle found. In order to verify the effectiveness of the proposed method in terms of the localization accuracy of the mushroom pileus circle, the average accuracy of the proposed method was 87.0%, which was higher than that of the traditional Circle Hough Transform method by 60.7%. Moreover, its localization stability was superior to that of Circle Hough Transform and the average running time of a single image is 2.3 s. Based on the result, the effectiveness of the proposed method meets the practical requirements of mushroom cultivation.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117129750","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042925
Yixuan Zhang
Artificial intelligence technology has been widely used in libraries and information. Based on the related research on data mining in the CNKI database, we analyzed the growth law of the number of literature and the distribution of journals by using the bibliometrics method. Keywords in the literature were researched by using co-word analysis and SPSS software. Factor analysis, cluster analysis, and multidimensional scale analysis were conducted on the keyword matrix to reveal the hot spots and key points of artificial intelligence data mining in the field of legal digital resources.
{"title":"Application of AI Data Mining in Legal Digital Resources in China Based on Big Data","authors":"Yixuan Zhang","doi":"10.1109/ECICE55674.2022.10042925","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042925","url":null,"abstract":"Artificial intelligence technology has been widely used in libraries and information. Based on the related research on data mining in the CNKI database, we analyzed the growth law of the number of literature and the distribution of journals by using the bibliometrics method. Keywords in the literature were researched by using co-word analysis and SPSS software. Factor analysis, cluster analysis, and multidimensional scale analysis were conducted on the keyword matrix to reveal the hot spots and key points of artificial intelligence data mining in the field of legal digital resources.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123855776","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042939
Yoonjeong Choi, Yujin Lim
As vehicles are connected to the Internet, various services such as infotainment and automated driving can be provided. However, these services require a large amount of data download. When downloading content which has the large size, the content delivery latency can become too long to meet the constraints. To solve this problem, methods for caching the content close to the vehicles are being studied. Macro base station (MBS) and road side unit (RSU) provide storage spaces at a close distance from the vehicles and they can reduce the time required to deliver the requested content. In this paper, we propose a caching strategy in RSUs, aiming to maximize the amount of content delivered from RSUsin order to reduce the delivery latency. Besides, since RSUs are densely deployed in urban areas, RSUs can cache more content by reducing duplicate content among them. Deep deterministic policy gradient (DDPG) is adopted to decide how to cache content in RSUs. Experiments show that the proposed method not only maximizes the amount of content downloaded from RSUs, but also decreases the update cost.
{"title":"Edge Caching Based on Deep Reinforcement Learning in Vehicular Networks","authors":"Yoonjeong Choi, Yujin Lim","doi":"10.1109/ECICE55674.2022.10042939","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042939","url":null,"abstract":"As vehicles are connected to the Internet, various services such as infotainment and automated driving can be provided. However, these services require a large amount of data download. When downloading content which has the large size, the content delivery latency can become too long to meet the constraints. To solve this problem, methods for caching the content close to the vehicles are being studied. Macro base station (MBS) and road side unit (RSU) provide storage spaces at a close distance from the vehicles and they can reduce the time required to deliver the requested content. In this paper, we propose a caching strategy in RSUs, aiming to maximize the amount of content delivered from RSUsin order to reduce the delivery latency. Besides, since RSUs are densely deployed in urban areas, RSUs can cache more content by reducing duplicate content among them. Deep deterministic policy gradient (DDPG) is adopted to decide how to cache content in RSUs. Experiments show that the proposed method not only maximizes the amount of content downloaded from RSUs, but also decreases the update cost.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122318666","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042898
Jian-An Lin, Ming-Tsung Lin, Yong-Zhong Li, Ya-Hsuan Wang
A CNC parameter optimization approach is presented to predict machining quality based on deep learning. The approach aims to optimize tracking error, contouring error, and cycle time simultaneously. CNC interpolator parameters including the limit of velocity, acceleration, jerk and corner tolerance are regarded as experimental factors. The standard test toolpath KANINO is adopted to collect signals of motion axes in various combinations of interpolation parameters. The back propagation neural network (BPNN) is utilized to establish the predicted model between the interpolation parameters and machining performance index. The parameter combination is optimized by the trained BPNN model with the non-dominated sorting genetic algorithm II (NSGA II). Finally, experimental validations are provided to demonstrate effectiveness of the proposed method in improvement of machining quality.
{"title":"CNC Interpolator Parameter Optimization using Deep Learning","authors":"Jian-An Lin, Ming-Tsung Lin, Yong-Zhong Li, Ya-Hsuan Wang","doi":"10.1109/ECICE55674.2022.10042898","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042898","url":null,"abstract":"A CNC parameter optimization approach is presented to predict machining quality based on deep learning. The approach aims to optimize tracking error, contouring error, and cycle time simultaneously. CNC interpolator parameters including the limit of velocity, acceleration, jerk and corner tolerance are regarded as experimental factors. The standard test toolpath KANINO is adopted to collect signals of motion axes in various combinations of interpolation parameters. The back propagation neural network (BPNN) is utilized to establish the predicted model between the interpolation parameters and machining performance index. The parameter combination is optimized by the trained BPNN model with the non-dominated sorting genetic algorithm II (NSGA II). Finally, experimental validations are provided to demonstrate effectiveness of the proposed method in improvement of machining quality.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115355318","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042905
F. Weng, Min-Fong Tsai, T. Chen
In this project, performance of bearing lubricant for wind turbine were analyzed. The experimental equipment includes fans, anemometers, generators, rectifiers and voltage stabilizers. carbon steel sheet is used as the wind turbine skeleton which was matched with the position of the lock hole of the bearing fixing seat, and is fixed and well-constructed. Material of blades was assembled using aluminum alloy and the fan was driven by a fixed air source in experiment. Power generation of wind turbine as well as vibration data of bearing of wind turbine were investigated. The vibration frequency spectrum of bearing was regularly measured under normal circumstances. Lubrication performance and power generation were investigated by comparison with two different lubrications. The experimental results can be obtained by checking the rotation speed and power generation efficiency of different greases. Though there was no obvious change in rotation speed and power generation, the RMS diagram of vibration spectrum shows a decreased trend. A simple test model using a fan motor was set up for vibration test. The rotation speed was increased in a specific formula of grease that compared with a general grease, which can also be read from the frequency spectrum in vibration test.
{"title":"An Analysis of Bearing Lubricant in a Wind Turbine","authors":"F. Weng, Min-Fong Tsai, T. Chen","doi":"10.1109/ECICE55674.2022.10042905","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042905","url":null,"abstract":"In this project, performance of bearing lubricant for wind turbine were analyzed. The experimental equipment includes fans, anemometers, generators, rectifiers and voltage stabilizers. carbon steel sheet is used as the wind turbine skeleton which was matched with the position of the lock hole of the bearing fixing seat, and is fixed and well-constructed. Material of blades was assembled using aluminum alloy and the fan was driven by a fixed air source in experiment. Power generation of wind turbine as well as vibration data of bearing of wind turbine were investigated. The vibration frequency spectrum of bearing was regularly measured under normal circumstances. Lubrication performance and power generation were investigated by comparison with two different lubrications. The experimental results can be obtained by checking the rotation speed and power generation efficiency of different greases. Though there was no obvious change in rotation speed and power generation, the RMS diagram of vibration spectrum shows a decreased trend. A simple test model using a fan motor was set up for vibration test. The rotation speed was increased in a specific formula of grease that compared with a general grease, which can also be read from the frequency spectrum in vibration test.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122613435","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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