Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditi...Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions.An integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge.This study presents an IIoT-based sensor node for industrial motors.The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms.The initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple sensors.The proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main brokers.The broker’s bridging allows for data backup in case of connection loss.The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial environments.The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults.The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.展开更多
In order to reduce the maintenance cost of structured Peer-to-Peer (P2P),Clone Node Protocol (CNP) based on user behavior is proposed.CNP considers the regularity of user behavior and uses the method of clone node.A B...In order to reduce the maintenance cost of structured Peer-to-Peer (P2P),Clone Node Protocol (CNP) based on user behavior is proposed.CNP considers the regularity of user behavior and uses the method of clone node.A Bidirectional Clone Node Chord model (BCNChord) based on CNP protocol is designed and realized.In BCNChord,Anticlockwise Searching Algorithm,Difference Push Synchronize Algorithm and Optimal Maintenance Algorithm are put forward to increase the performances.In experiments,according to the frequency of nodes,the maintenance cost of BCNChord can be 3.5%～32.5% lower than that of Chord.In the network of 212 nodes,the logic path hop is steady at 6,which is much more prior to 12 of Chord and 10 of CNChord.Theoretical analysis and experimental results show that BCNChord can effectively reduce the maintenance cost of its structure and simultaneously improve the query efficiency up to (1/4)O(logN).BCNChord is more suitable for highly dynamic environment and higher real-time system.展开更多
A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper.In this method,the original image sequence data i...A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper.In this method,the original image sequence data is first separated from the foreground and background.Then,the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction.Finally,human posture nodes are extracted from each frame of the image sequence,which are then used to identify the abnormal behavior of the human.Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.展开更多
基金This paper is supported by the NCAIRF 079 project fund.The project is funded by National Center of Artificial Intelligence.
文摘Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions.An integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge.This study presents an IIoT-based sensor node for industrial motors.The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms.The initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple sensors.The proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main brokers.The broker’s bridging allows for data backup in case of connection loss.The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial environments.The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults.The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.
基金supported by the National Natural Science Foundation of China under Grant No.61100205Science and Technology Project of Beijing Municipal Education Commission under Grant No.KM201110016006Doctor Start-up Foundation of BUCEA under Grant No.101002508
文摘In order to reduce the maintenance cost of structured Peer-to-Peer (P2P),Clone Node Protocol (CNP) based on user behavior is proposed.CNP considers the regularity of user behavior and uses the method of clone node.A Bidirectional Clone Node Chord model (BCNChord) based on CNP protocol is designed and realized.In BCNChord,Anticlockwise Searching Algorithm,Difference Push Synchronize Algorithm and Optimal Maintenance Algorithm are put forward to increase the performances.In experiments,according to the frequency of nodes,the maintenance cost of BCNChord can be 3.5%～32.5% lower than that of Chord.In the network of 212 nodes,the logic path hop is steady at 6,which is much more prior to 12 of Chord and 10 of CNChord.Theoretical analysis and experimental results show that BCNChord can effectively reduce the maintenance cost of its structure and simultaneously improve the query efficiency up to (1/4)O(logN).BCNChord is more suitable for highly dynamic environment and higher real-time system.
基金supported by the project“Research and application of key technologies of safe production management and control of substation operation and maintenance based on video semantic analysis”(5700-202133259A-0-0-00)of the State Grid Corporation of China.
文摘A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper.In this method,the original image sequence data is first separated from the foreground and background.Then,the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction.Finally,human posture nodes are extracted from each frame of the image sequence,which are then used to identify the abnormal behavior of the human.Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.
