Pub Date : 2024-03-15DOI: 10.1177/00202940241236088
Lei Wang, Jianxun Chen, Yu Zhang, Yali Tian, Zhengyu Li, Chenghao Wang
Aiming at the problems existing in discrete manufacturing workshops of ships, such as the lack of real-time management and control ability of on-site production status, and the long time delay of real-time interaction and fusion between physical entities and virtual entities data, this paper proposes ship production workshop data mapping and fusion method based on digital twins. Taking a ship machining workshop as an application research example, the integrated business process and data mapping and fusion method of the machining workshop based on digital twins are studied. On this basis, the dynamic scheduling optimization algorithm of ship machining workshops based on digital twins is studied. The results show that the data mapping and fusion method in this study can significantly reduce the time delay of data interaction between the virtual and real objects. It can effectively improve the real-time and production flexibility of dynamic and complex tasks such as emergency insertion and task rework. The ability of real-time data interaction and synchronous real-time mapping between physical entities and virtual entities in dynamic scenes such as production line reconstruction and customized production is improved.
{"title":"Research on data mapping and fusion method of ship production workshop based on digital twins","authors":"Lei Wang, Jianxun Chen, Yu Zhang, Yali Tian, Zhengyu Li, Chenghao Wang","doi":"10.1177/00202940241236088","DOIUrl":"https://doi.org/10.1177/00202940241236088","url":null,"abstract":"Aiming at the problems existing in discrete manufacturing workshops of ships, such as the lack of real-time management and control ability of on-site production status, and the long time delay of real-time interaction and fusion between physical entities and virtual entities data, this paper proposes ship production workshop data mapping and fusion method based on digital twins. Taking a ship machining workshop as an application research example, the integrated business process and data mapping and fusion method of the machining workshop based on digital twins are studied. On this basis, the dynamic scheduling optimization algorithm of ship machining workshops based on digital twins is studied. The results show that the data mapping and fusion method in this study can significantly reduce the time delay of data interaction between the virtual and real objects. It can effectively improve the real-time and production flexibility of dynamic and complex tasks such as emergency insertion and task rework. The ability of real-time data interaction and synchronous real-time mapping between physical entities and virtual entities in dynamic scenes such as production line reconstruction and customized production is improved.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"16 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140238646","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}
Frequency is one of the most significant indicators of power system, which needs accurate estimation to provide a reliable basis for monitoring, control, and protection. Owning to the efficiency, DFT is widely used in the frequency measurement of power systems. However, the inherent defects, that is, the spectral leakage and picket fence effect, decrease the accuracy of DFT. One of the variants, named the windowed phase difference method (WPD), addresses the problem using the phase variation of two windowed sequences in the frequency domain and achieves accurate frequency estimation. Suffering from the ignored negative component and contamination of noise, there are still undesired errors in WPD-based frequency estimation. To reduce the influence of the undesired errors, this paper first analyzes the systematic error of WPD introduced by spectrum leakage and proposes an improved WPD (IWPD) through systematic error compensation to achieve more accurate frequency estimations. Then, the windowing effect on IWPD-based frequency estimation of a noisy signal is studied by deducing the theoretical expression of frequency estimation variance. Finally, the proposed IWPD is validated by extensive computer simulations and practical experiments.
{"title":"Improved windowed phase difference method for frequency estimation of distorted power signals","authors":"Chengcheng Li, Yuan Wan, Pingheng Pan, Bian Hu, Junhao Zhang","doi":"10.1177/00202940241237135","DOIUrl":"https://doi.org/10.1177/00202940241237135","url":null,"abstract":"Frequency is one of the most significant indicators of power system, which needs accurate estimation to provide a reliable basis for monitoring, control, and protection. Owning to the efficiency, DFT is widely used in the frequency measurement of power systems. However, the inherent defects, that is, the spectral leakage and picket fence effect, decrease the accuracy of DFT. One of the variants, named the windowed phase difference method (WPD), addresses the problem using the phase variation of two windowed sequences in the frequency domain and achieves accurate frequency estimation. Suffering from the ignored negative component and contamination of noise, there are still undesired errors in WPD-based frequency estimation. To reduce the influence of the undesired errors, this paper first analyzes the systematic error of WPD introduced by spectrum leakage and proposes an improved WPD (IWPD) through systematic error compensation to achieve more accurate frequency estimations. Then, the windowing effect on IWPD-based frequency estimation of a noisy signal is studied by deducing the theoretical expression of frequency estimation variance. Finally, the proposed IWPD is validated by extensive computer simulations and practical experiments.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"78 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241728","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 : 2024-03-11DOI: 10.1177/00202940231217340
Jaleh Shirin Nejad, M. Saraj, Sara Shokrolahi Yancheshmeh, Fatemeh Kiany Harchegani
The main issue in the present article is to investigate how to solve a mixed integer fractional signomial geometric programing problem (MIFSGP). In the first step to achieving this idea, we must convert a fractional signomial programing problem into a non-fractional problem via a simple conversion technique. Then, a convex relaxation with a new modified piecewise linear approximation with integer break points as a pre-solve method is used to reach an integer global optimum solution. A few numerical examples are included to illustrate the advantages of the proposed method
{"title":"Global optimization of mixed integer signomial fractional programing problems","authors":"Jaleh Shirin Nejad, M. Saraj, Sara Shokrolahi Yancheshmeh, Fatemeh Kiany Harchegani","doi":"10.1177/00202940231217340","DOIUrl":"https://doi.org/10.1177/00202940231217340","url":null,"abstract":"The main issue in the present article is to investigate how to solve a mixed integer fractional signomial geometric programing problem (MIFSGP). In the first step to achieving this idea, we must convert a fractional signomial programing problem into a non-fractional problem via a simple conversion technique. Then, a convex relaxation with a new modified piecewise linear approximation with integer break points as a pre-solve method is used to reach an integer global optimum solution. A few numerical examples are included to illustrate the advantages of the proposed method","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140254282","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 : 2024-02-29DOI: 10.1177/00202940241230488
Peng Zhao, Xinyu Pang, Feng Li, Kaibo Lu, Shouxin Hu
Aiming at the problem that it takes a long time and high cost to obtain complete labeled data under intelligent fault diagnosis and unlabeled data is not used. This paper proposes an improved semi-supervised mean teacher deep learning (MTDL) and Gramian angle field (GAF) fusion diagnostic method. This method fully utilizes a small number of labeled samples and a large number of unlabeled samples to deeply mine invisible fault features and potential physical correlations. At the same time, it solves the problem of losing the inter-data correlation structure when one-dimensional time series signals are used as inputs for neural networks. The GAF-MTDL method uses consistency regularization and modifies the network structure in the mean teacher algorithm into a semi-supervised deep learning model enhanced by WideResNet. The experimental results show that the proposed GAF-MTDL method saves a lot of manual labeling costs, improves the recognition accuracy and generalization ability, and can achieve excellent prediction accuracy with very little labeled data. In the end, the accuracy of planetary gear fault identification reached 98.22% under the labeling rate of 20%, and the accuracy of fault identification reached 99.98% through the verification of the bearing data set of Case Western Reserve University. The value of this research is to bring an efficient and low-cost technology to the field of industrial intelligent fault diagnosis, which can significantly improve the accuracy of fault identification.
{"title":"Gearbox fault diagnosis method based on improved semi-supervised MTDL and GAF","authors":"Peng Zhao, Xinyu Pang, Feng Li, Kaibo Lu, Shouxin Hu","doi":"10.1177/00202940241230488","DOIUrl":"https://doi.org/10.1177/00202940241230488","url":null,"abstract":"Aiming at the problem that it takes a long time and high cost to obtain complete labeled data under intelligent fault diagnosis and unlabeled data is not used. This paper proposes an improved semi-supervised mean teacher deep learning (MTDL) and Gramian angle field (GAF) fusion diagnostic method. This method fully utilizes a small number of labeled samples and a large number of unlabeled samples to deeply mine invisible fault features and potential physical correlations. At the same time, it solves the problem of losing the inter-data correlation structure when one-dimensional time series signals are used as inputs for neural networks. The GAF-MTDL method uses consistency regularization and modifies the network structure in the mean teacher algorithm into a semi-supervised deep learning model enhanced by WideResNet. The experimental results show that the proposed GAF-MTDL method saves a lot of manual labeling costs, improves the recognition accuracy and generalization ability, and can achieve excellent prediction accuracy with very little labeled data. In the end, the accuracy of planetary gear fault identification reached 98.22% under the labeling rate of 20%, and the accuracy of fault identification reached 99.98% through the verification of the bearing data set of Case Western Reserve University. The value of this research is to bring an efficient and low-cost technology to the field of industrial intelligent fault diagnosis, which can significantly improve the accuracy of fault identification.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"22 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140409343","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}
Unmanned aerial vehicle (UAV) has great application prospect because of its capability of three-dimensional space operation. The reliability of attitude and heading reference system (AHRS) for UAV attitude estimation is crucial for the application of UAV. When a UAV is subjected to unknown electromagnetic interference and force interference in flight, its attitude detection system can suffer from reduced accuracy or even failure. In this paper, a fuzzy adaptive complementary filter (CF) for attitude estimation based on norm judgment is proposed to solve the problem that the sensor is easily disturbed by the complex flight environment and the fixed filter parameters are difficult to obtain the UAV attitude accurately under different flight states. Firstly, the correction model of the gyroscope, which includes four filter parameters, namely accelerometer weight, magnetometer weight, proportional gain P and integral gain I, is established. Secondly, the influence of the four parameters on the estimation accuracy is analyzed. Finally, the adaptive adjustment rules are designed to adjust the filter parameters online and thus achieve the accurate and reliable measurement of UAV attitude. The feasibility of the proposed algorithm is verified through static, dynamic and interference experiments with the specially designed AHRS test platform. And the results show that the attitude estimation algorithm designed in this paper can ensure the high accuracy of the system in both steady state and high-speed rotation, shield the pitch and roll angles from the acceleration of motion, and keep the yaw angle from the external magnetic field.
无人驾驶飞行器（UAV）具有三维空间作业能力，应用前景广阔。用于无人飞行器姿态估计的姿态和航向参考系统（AHRS）的可靠性对无人飞行器的应用至关重要。当无人机在飞行过程中受到未知电磁干扰和力干扰时，其姿态检测系统会出现精度降低甚至失效的问题。本文提出了一种基于常模判断的模糊自适应姿态估计互补滤波器（CF），以解决传感器易受复杂飞行环境干扰、固定滤波器参数难以准确获取不同飞行状态下无人机姿态的问题。首先，建立了陀螺仪的修正模型，包括加速度计权重、磁力计权重、比例增益 P 和积分增益 I 四个滤波参数。其次，分析了四个参数对估计精度的影响。最后，设计了自适应调整规则来在线调整滤波器参数，从而实现无人机姿态的准确可靠测量。利用专门设计的 AHRS 测试平台，通过静态、动态和干扰实验验证了所提算法的可行性。结果表明，本文设计的姿态估计算法能够保证系统在稳态和高速旋转时都具有较高的精度，能够屏蔽运动加速度对俯仰角和滚转角的影响，并使偏航角不受外部磁场的影响。
{"title":"A fuzzy adaptive complementary filter for attitude estimation based on norm judgment","authors":"Pengcheng Jiang, Chang Liu, Hua Cong, Fuqiang Zhang","doi":"10.1177/00202940241227069","DOIUrl":"https://doi.org/10.1177/00202940241227069","url":null,"abstract":"Unmanned aerial vehicle (UAV) has great application prospect because of its capability of three-dimensional space operation. The reliability of attitude and heading reference system (AHRS) for UAV attitude estimation is crucial for the application of UAV. When a UAV is subjected to unknown electromagnetic interference and force interference in flight, its attitude detection system can suffer from reduced accuracy or even failure. In this paper, a fuzzy adaptive complementary filter (CF) for attitude estimation based on norm judgment is proposed to solve the problem that the sensor is easily disturbed by the complex flight environment and the fixed filter parameters are difficult to obtain the UAV attitude accurately under different flight states. Firstly, the correction model of the gyroscope, which includes four filter parameters, namely accelerometer weight, magnetometer weight, proportional gain P and integral gain I, is established. Secondly, the influence of the four parameters on the estimation accuracy is analyzed. Finally, the adaptive adjustment rules are designed to adjust the filter parameters online and thus achieve the accurate and reliable measurement of UAV attitude. The feasibility of the proposed algorithm is verified through static, dynamic and interference experiments with the specially designed AHRS test platform. And the results show that the attitude estimation algorithm designed in this paper can ensure the high accuracy of the system in both steady state and high-speed rotation, shield the pitch and roll angles from the acceleration of motion, and keep the yaw angle from the external magnetic field.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140413351","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 : 2024-02-29DOI: 10.1177/00202940231223104
Jianjun Zhang, Hang Li, Zhiqiang Liu, Zhonghua Wu, Jinxian Yang
The bilateral teleoperation system is susceptible to model parameter uncertainty and unknown disturbances in both the master and slave manipulators, resulting in instability and inaccuracies in the force and position tracking performance. To address these issues, a novel nonlinear model reference adaptive impedance controller has been designed to achieve coordinated force and position synchronization of dual manipulators. The adaptive control laws, based on sliding mode functions, have been designed to compensate for the uncertainty of the manipulator model. Furthermore, an adaptive estimation law has been employed to appraise the unknown upper bound of external disturbances. This ensures that the closed-loop model parameters of the dual manipulator converge to the reference impedance model respectively. Simultaneously, it enables the position error between the reference model response and the end-effector task space position to asymptotically converge to zero. To verify the effectiveness of the proposed controller, simulations have been conducted on the MATLAB platform and experiments on a single degree of freedom teleoperation system have been performed. The results demonstrate that the controller exhibits strong robustness and has the capability of force-position tracking ability.
{"title":"The nonlinear model reference adaptive impedance control for underwater manipulator operation objects in bilateral teleoperation system","authors":"Jianjun Zhang, Hang Li, Zhiqiang Liu, Zhonghua Wu, Jinxian Yang","doi":"10.1177/00202940231223104","DOIUrl":"https://doi.org/10.1177/00202940231223104","url":null,"abstract":"The bilateral teleoperation system is susceptible to model parameter uncertainty and unknown disturbances in both the master and slave manipulators, resulting in instability and inaccuracies in the force and position tracking performance. To address these issues, a novel nonlinear model reference adaptive impedance controller has been designed to achieve coordinated force and position synchronization of dual manipulators. The adaptive control laws, based on sliding mode functions, have been designed to compensate for the uncertainty of the manipulator model. Furthermore, an adaptive estimation law has been employed to appraise the unknown upper bound of external disturbances. This ensures that the closed-loop model parameters of the dual manipulator converge to the reference impedance model respectively. Simultaneously, it enables the position error between the reference model response and the end-effector task space position to asymptotically converge to zero. To verify the effectiveness of the proposed controller, simulations have been conducted on the MATLAB platform and experiments on a single degree of freedom teleoperation system have been performed. The results demonstrate that the controller exhibits strong robustness and has the capability of force-position tracking ability.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"9 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140411034","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 : 2024-02-27DOI: 10.1177/00202940241229927
Xiaodong Wang, Changyin Wei, Yong Chen, Qiang Ma
Parametric uncertainties and inverter nonlinearity exist in the permanent magnet synchronous motor (PMSM) drive system, which may lead to performance degradation or failure of model-based control. This paper presents an enhanced model-free synergetic control method for PMSMs, addressing the limitations of relying on accurate motor models in existing speed control strategies. The proposed approach aims to achieve robust control performance in the presence of parameter perturbations without the need for an accurate motor model. Firstly, a new ultra-local model of the speed loop for the PMSM is established based on a newly developed ultra-local theory. Subsequently, a model-free synergetic controller is designed using the principles of synergetic control theory. To address the issue of output chattering in the sliding mode observer used to estimate the unknown part of the ultra-local model, a synergetic observer method is proposed. Simulation results are presented and compared with those obtained using a proportional-integral (PI) controller and a conventional model-free sliding mode controller. The results demonstrate that the model-free synergetic controller exhibits robust performance and provides accurate estimation of the unknown part without output chattering.
{"title":"Improved model-free synergetic control of permanent magnet synchronous motor using unknown input observer","authors":"Xiaodong Wang, Changyin Wei, Yong Chen, Qiang Ma","doi":"10.1177/00202940241229927","DOIUrl":"https://doi.org/10.1177/00202940241229927","url":null,"abstract":"Parametric uncertainties and inverter nonlinearity exist in the permanent magnet synchronous motor (PMSM) drive system, which may lead to performance degradation or failure of model-based control. This paper presents an enhanced model-free synergetic control method for PMSMs, addressing the limitations of relying on accurate motor models in existing speed control strategies. The proposed approach aims to achieve robust control performance in the presence of parameter perturbations without the need for an accurate motor model. Firstly, a new ultra-local model of the speed loop for the PMSM is established based on a newly developed ultra-local theory. Subsequently, a model-free synergetic controller is designed using the principles of synergetic control theory. To address the issue of output chattering in the sliding mode observer used to estimate the unknown part of the ultra-local model, a synergetic observer method is proposed. Simulation results are presented and compared with those obtained using a proportional-integral (PI) controller and a conventional model-free sliding mode controller. The results demonstrate that the model-free synergetic controller exhibits robust performance and provides accurate estimation of the unknown part without output chattering.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"30 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140425960","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 : 2024-02-27DOI: 10.1177/00202940241231473
Muhammad Yousuf, Turki Alsuwian, Arslan Ahmed Amin, Sanwal Fareed, Muhammad Hamza
This research paper presents the development and implementation of an integrated condition monitoring and fault detection system for AC induction motors using a combination of sensors, GSM communication, and a cloud-based Internet of Things (IoT) platform. The proposed system aims to enhance industrial motors’ operational reliability and efficiency by providing real-time data monitoring and early fault detection. The key components of the system include temperature, vibration, current, voltage, and speed sensors, which are strategically placed to gather critical motor performance data. These sensors feed data to an Arduino-based control unit responsible for sensor data acquisition and processing. To ensure timely response to anomalies, the system is equipped with an alarm system and GSM alerts, which notify designated personnel in case of abnormal motor behavior. Moreover, the paper incorporates remote monitoring capabilities, enabling users to access motor health data and real-time status from a distance. Historical data is also stored for analysis and comparison through the integration of a cloud-based Blynk-IoT platform. Additionally, the system facilitates RPM control and utilizes relay modules for seamless motor control and protection. The proposed system was tested and validated using Proteus for circuit diagram simulation and Arduino for sensor coding. The results demonstrate its effectiveness in detecting abnormal motor behavior and its potential to prevent catastrophic failures by enabling predictive maintenance. The proposed system successfully detects and displays abnormalities in important parameters like vibration, temperature, speed, three-phase currents, and voltages with 99% accuracy.
{"title":"IoT-based health monitoring and fault detection of industrial AC induction motor for efficient predictive maintenance","authors":"Muhammad Yousuf, Turki Alsuwian, Arslan Ahmed Amin, Sanwal Fareed, Muhammad Hamza","doi":"10.1177/00202940241231473","DOIUrl":"https://doi.org/10.1177/00202940241231473","url":null,"abstract":"This research paper presents the development and implementation of an integrated condition monitoring and fault detection system for AC induction motors using a combination of sensors, GSM communication, and a cloud-based Internet of Things (IoT) platform. The proposed system aims to enhance industrial motors’ operational reliability and efficiency by providing real-time data monitoring and early fault detection. The key components of the system include temperature, vibration, current, voltage, and speed sensors, which are strategically placed to gather critical motor performance data. These sensors feed data to an Arduino-based control unit responsible for sensor data acquisition and processing. To ensure timely response to anomalies, the system is equipped with an alarm system and GSM alerts, which notify designated personnel in case of abnormal motor behavior. Moreover, the paper incorporates remote monitoring capabilities, enabling users to access motor health data and real-time status from a distance. Historical data is also stored for analysis and comparison through the integration of a cloud-based Blynk-IoT platform. Additionally, the system facilitates RPM control and utilizes relay modules for seamless motor control and protection. The proposed system was tested and validated using Proteus for circuit diagram simulation and Arduino for sensor coding. The results demonstrate its effectiveness in detecting abnormal motor behavior and its potential to prevent catastrophic failures by enabling predictive maintenance. The proposed system successfully detects and displays abnormalities in important parameters like vibration, temperature, speed, three-phase currents, and voltages with 99% accuracy.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"8 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140425116","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 : 2024-02-26DOI: 10.1177/00202940241233383
Muazzam Ghafoor, Arslan Ahmed Amin, Muhammad Shoaib Khalid
Solar photovoltaic (PV) panels suffer from a reduction in performance due to dirt and environmental pollutants accumulating on their surface. As the number of solar panels installed grows, boosting their efficiency becomes more crucial to optimizing electricity output while decreasing the need for additional panels. Cleaning the PV panels can increase their efficiency, and an automated cleaning system with cutting-edge technologies can improve cleaning effectiveness. This article proposes a system that utilizes the Messaging Queuing Telemetry Transport protocol to enhance the efficiency of solar panels. Also, the system harnesses the power of IoT technology to automate the cleaning process. The system incorporates IoT-enabled sensors, actuators, and communication modules, all controlled by ESP-32. We programed the microcontroller using the software Arduino and Visual Studio Code. The Adafruit IO controls and monitors the system. Users can switch between fully automatic and manual modes using the Adafruit dashboard. The system utilizes DC motors, nylon brushes, a metal frame, a relay module, a buck converter, and a boost converter. A 10W solar panel powers the system with the help of a 20 A charge controller and a 12 V/2.5 Ah battery. The Adafruit dashboard fully controls and monitors the developed system. By automating the cleaning process and leveraging real-time data, the system maximizes solar power generation efficiency, minimizing downtime. The system increases the efficiency of tested 30 W solar panels by 30%.
{"title":"Design of IoT-based solar array cleaning system with enhanced performance and efficiency","authors":"Muazzam Ghafoor, Arslan Ahmed Amin, Muhammad Shoaib Khalid","doi":"10.1177/00202940241233383","DOIUrl":"https://doi.org/10.1177/00202940241233383","url":null,"abstract":"Solar photovoltaic (PV) panels suffer from a reduction in performance due to dirt and environmental pollutants accumulating on their surface. As the number of solar panels installed grows, boosting their efficiency becomes more crucial to optimizing electricity output while decreasing the need for additional panels. Cleaning the PV panels can increase their efficiency, and an automated cleaning system with cutting-edge technologies can improve cleaning effectiveness. This article proposes a system that utilizes the Messaging Queuing Telemetry Transport protocol to enhance the efficiency of solar panels. Also, the system harnesses the power of IoT technology to automate the cleaning process. The system incorporates IoT-enabled sensors, actuators, and communication modules, all controlled by ESP-32. We programed the microcontroller using the software Arduino and Visual Studio Code. The Adafruit IO controls and monitors the system. Users can switch between fully automatic and manual modes using the Adafruit dashboard. The system utilizes DC motors, nylon brushes, a metal frame, a relay module, a buck converter, and a boost converter. A 10W solar panel powers the system with the help of a 20 A charge controller and a 12 V/2.5 Ah battery. The Adafruit dashboard fully controls and monitors the developed system. By automating the cleaning process and leveraging real-time data, the system maximizes solar power generation efficiency, minimizing downtime. The system increases the efficiency of tested 30 W solar panels by 30%.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"84 2‐3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140429009","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}
This study proposes a method of autonomous navigation UAV for oil and gas pipeline (OGP) dial detection based on the improved YOLOv7 model. The canny edge detection algorithm is applied in identifying the edges of the pipeline, and the Hough transform algorithm is used to detect the pipeline in a straight line. The intelligent UAV P600 is guided to patrol the oil and gas dials (OGD) along the pipeline, and the trained improved YOLOv7-based model is adopted to identify the OGD data. Dial recognition is divided into two stages, that is, dial contour detection and dial reading recognition. For the dial recognition rate (RR), the Levenstein distance, a commonly used method, is introduced, thereby calculating the distance between two character sequences. Meanwhile, an integrated global attention mechanism (GAM) is proposed based on the YOLOv7 model, aiming at extracting more informative features. With this mechanism, the channel and spatial aspects of the features are effectively captured, and the importance of cross-dimensional interactions is increased. By introducing GAM attention mechanism in the backbone and head of YOLOv7, the network’s ability in efficiently extracting depth and primary features is enhanced. ACmix (a hybrid model combining the advantages of self-attentiveness and convolution) is also included, with ACmix module improved. The improved ACmix module has the objectives of enhancing feature extraction capability of backbone network and accelerating network convergence. By substituting 3 × 3 convolutional block with 3 × 3 ACmixBlock and adding a jump connection and a 1 × 1 convolutional structure between the ACmixBlock modules, E-ELAN module in YOLOv7 network is also improved, thus optimizing E-ELAN network, enriching features extracted by E-ELAN network, and reducing inference time of YOLOv7 model. As indicated by comparing the experimental results of the six model algorithms (improved YOLOv7, YOLOv7, YOLOX, YOLOv5, YOLOv6 and Faster R-CNN), the improved YOLOv7 model has higher mAP, faster RR, faster network convergence, and higher IOU. In addition, a generic real dataset, called custom dial reading dataset, is presented. With well-defined evaluation protocol, this dataset allows for a fair comparison of various methods in future work.
{"title":"An oil and gas pipeline inspection UAV based on improved YOLOv7","authors":"Yongxiang Zhao, Wei Luo, Zhiguo Wang, Guoqing Zhang, Jiandong Liu, Xiaoliang Li, Qi Wang","doi":"10.1177/00202940241230426","DOIUrl":"https://doi.org/10.1177/00202940241230426","url":null,"abstract":"This study proposes a method of autonomous navigation UAV for oil and gas pipeline (OGP) dial detection based on the improved YOLOv7 model. The canny edge detection algorithm is applied in identifying the edges of the pipeline, and the Hough transform algorithm is used to detect the pipeline in a straight line. The intelligent UAV P600 is guided to patrol the oil and gas dials (OGD) along the pipeline, and the trained improved YOLOv7-based model is adopted to identify the OGD data. Dial recognition is divided into two stages, that is, dial contour detection and dial reading recognition. For the dial recognition rate (RR), the Levenstein distance, a commonly used method, is introduced, thereby calculating the distance between two character sequences. Meanwhile, an integrated global attention mechanism (GAM) is proposed based on the YOLOv7 model, aiming at extracting more informative features. With this mechanism, the channel and spatial aspects of the features are effectively captured, and the importance of cross-dimensional interactions is increased. By introducing GAM attention mechanism in the backbone and head of YOLOv7, the network’s ability in efficiently extracting depth and primary features is enhanced. ACmix (a hybrid model combining the advantages of self-attentiveness and convolution) is also included, with ACmix module improved. The improved ACmix module has the objectives of enhancing feature extraction capability of backbone network and accelerating network convergence. By substituting 3 × 3 convolutional block with 3 × 3 ACmixBlock and adding a jump connection and a 1 × 1 convolutional structure between the ACmixBlock modules, E-ELAN module in YOLOv7 network is also improved, thus optimizing E-ELAN network, enriching features extracted by E-ELAN network, and reducing inference time of YOLOv7 model. As indicated by comparing the experimental results of the six model algorithms (improved YOLOv7, YOLOv7, YOLOX, YOLOv5, YOLOv6 and Faster R-CNN), the improved YOLOv7 model has higher mAP, faster RR, faster network convergence, and higher IOU. In addition, a generic real dataset, called custom dial reading dataset, is presented. With well-defined evaluation protocol, this dataset allows for a fair comparison of various methods in future work.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"132 S225","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140429057","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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