scholarly journals A practical and synchronized data acquisition network architecture for industrial robot predictive maintenance in manufacturing assembly lines

2022 ◽  
Vol 74 ◽  
pp. 102287
Author(s):  
Unai Izagirre ◽  
Imanol Andonegui ◽  
Itziar Landa-Torres ◽  
Urko Zurutuza
Keyword(s):  
Data Acquisition ◽  
Network Architecture ◽  
Industrial Robot ◽  
Predictive Maintenance ◽  
Assembly Lines ◽  
Manufacturing Assembly

A Model for a Series Processing System

1967 ◽  
Vol 9 (1) ◽  
pp. 53-59
Author(s):  
Wellington E. Smith
Keyword(s):  
System Performance ◽  
Processing System ◽  
Predictive Ability ◽  
Performance Measure ◽  
Total System ◽  
Assembly Lines ◽  
Operator Performance ◽  
Total Performance ◽  
Manufacturing Assembly ◽  
System Effectiveness

Many processing systems, such as manufacturing assembly lines, can be described as a series of discrete operations performed on discrete units being processed. To evaluate the effectiveness of operators in such systems or to determine the best way to improve their performance, it is necessary to have a performance measure that relates to total system effectiveness. Current techniques measure operator performance in terms of time and errors, but they provide little predictive ability as to the effects of these parameters. To relate time and yield measures to a single criterion of system performance, a method has been developed for evaluating operator effectiveness in a series processing system that processes discrete items in large quantities. By recognizing and dealing with the fact that rejects at the end of series process are more expensive than at the beginning of the process, statements are developed for measuring performance in terms of its actual effect on the system. Concepts and methods are presented for measuring total system performance, performance of any segment of the system, total performance of any operator, and the effects of time and accuracy on operator performance.

The Improvement about the Reliability of Sampling Based on Process Bus in Smart Substation

2014 ◽  
Vol 1070-1072 ◽  
pp. 1398-1404
Author(s):  
Chen Fan ◽  
Yi Min Ni ◽  
Ren Hui Dou ◽  
Xin Xu ◽  
Zhi Qiang Yao ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Network Optimization ◽  
Data Transmission ◽  
Network Architecture ◽  
Transmission Network ◽  
Technical Problems ◽  
System Method ◽  
Smart Substation ◽  
New System

The sampling based on process bus is not widely used in smart substation because there are still some technical problems on synchronization, the design of network architecture and sampling data receiving and processing areas. There are some researches about the problems are carried out, but they only focus on part of them and cannot give the whole solution. In order to improve the reliability of network sampling, a new system method which includes all the segments of the sampling based on process such as the synchronization of data acquisition, data transmission, network optimization and data processing are presented. In the data acquisition segment, the synchronization based on IEEE1588 is provided, In the data transmission segment, a new hardware architecture which is based on FPGA+DSP+VxWorks is described, in the segment of network optimization, the topology structure and virtual LAN are suggested. At last, the automatic resampling is proposed in the data receiving and processing segment. It will be useful to improve the reliability of sampling based on process bus in smart substation.

Design of an advanced robotic vehicle evaluation laboratory

Robotica ◽  
1989 ◽  
Vol 7 (4) ◽  
pp. 281-287
Author(s):  
Richard E. Jarka ◽  
Zeinab A. Sabri ◽  
S. Keith Adams ◽  
Enju Liang ◽  
Michael Barnett ◽  
...  
Keyword(s):  
Field Tests ◽  
Wide Field ◽  
Assembly Lines ◽  
Monitoring Equipment ◽  
Military Industry ◽  
Hazardous Environments ◽  
Robotic Vehicles ◽  
Manufacturing Assembly ◽  
Robotic Vehicle ◽  
Advanced Robotics

SUMMARYRobotic vehicles have a wide field of applications in the civilian and military industry including manufacturing, assembly lines, security, operation in hostile environment, and testing. In the defense area, robotic vehicles have the potential for force multiplication and removing the soldier from hazardous environments on the battlefield. To make such vehicles avaialable requires research, development, testing and demonstration of advanced robotics and artificial intelligence (AI) technologies and systems. A realistic effort towards that objective requires the establishment of an advanced laboratory responsible for evaluation and development of subsystems and integration of the various elements into vehicles for field tests. Hence, requirements for the laboratory are given including a layout design and link analysis of the different components. As the first part of planning the laboratory, the technology was assessed to assure inclusion of the state-of-the-art equipment. Then, equipment requirements were defined, including interactions between pieces of equipment and providing for support, recording and monitoring equipment.

Multi-Level Radio Network Architecture For Seismic Data Acquisition System

2019 ◽  
Author(s):  
Danis K. Nurgaliev ◽  
Oleg N. Sherstyukov ◽  
Evgeniy Yu. Ryabchenko ◽  
Evgeniy V. Danilov ◽  
Alexey D. Smolyakov ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Seismic Data ◽  
Network Architecture ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Radio Network ◽  
Seismic Data Acquisition ◽  
Multi Level

Robust Motion Generation for Vision-Guided Robot Bin-Picking

2007 ◽  
Author(s):  
Simon Leonard ◽  
Ambrose Chan ◽  
Elizabeth Croft ◽  
James J. Little
Keyword(s):  
Visual Servoing ◽  
Industrial Robot ◽  
Three Dimensional ◽  
Cost Effective ◽  
Object Localization ◽  
Assembly Lines ◽  
Motion Generation ◽  
Simple Task ◽  
Bin Picking ◽  
Modern Manufacturing

This paper discusses work towards a vision-based solution to the problem of robot bin-picking. The problem of robot bin-picking is defined as searching for and recognizing a part among many lying jumbled in a bin such that the robot is able to grasp and manipulate the part. Despite decades of research in vision, robotics, and manufacturing, this problem remains open. Currently, in modern manufacturing, this seemingly simple task is performed by complex assembly lines or manual labor. The amount of efforts and costs associated with the current solutions to bin-picking is a testament to the importance of a new solution. The main objective of this research is a reliable and cost effective automated solution to the bin-picking problem encountered in manufacturing. As a broader contribution, this research also provides a robust visual servoing method that enables safe interactions between a robot and its environment. Our system uses visual feedback to generate tasks autonomously and to control the interaction of the manipulator with its environment. First, our system relies on robust vision-based object localization to generate three-dimensional pose hypotheses for each identified part. Then, the hypotheses are filtered according to the feasibility of their picking configuration. Finally, a trajectory is generated for a picking position. In this paper, we consider the specifications of the trajectory ensure that collisions with the bin and joints limits are avoided, while servoing the robot to the part. To ensure the reliability of the system, the procedure is tested in a simulation before being executed by a manipulator. Our experiments target the automotive industry and involve real engine parts a typical industrial robot and metal bin.

NEURAL NETWORK BASED DYNAMIC CONTROLLERS FOR INDUSTRIAL ROBOTS

1995 ◽  
Vol 06 (03) ◽  
pp. 257-271
Author(s):  
SE-YOUNG OH ◽  
WEON-CHANG SHIN ◽  
HYO-GYU KIM
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Industrial Robot ◽  
Industrial Robots ◽  
System Stability ◽  
Robot Dynamics ◽  
Time Dynamic ◽  
Dynamic Controller ◽  
And Control

The industrial robot’s dynamic performance is frequently measured by positioning accuracy at high speeds and a good dynamic controller is essential that can accurately compute robot dynamics at a servo rate high enough to ensure system stability. A real-time dynamic controller for an industrial robot is developed here using neural networks. First, an efficient time-selectable hidden layer architecture has been developed based on system dynamics localized in time, which lends itself to real-time learning and control along with enhanced mapping accuracy. Second, the neural network architecture has also been specially tuned to accommodate servo dynamics. This not only facilitates the system design through reduced sensing requirements for the controller but also enhances the control performance over the control architecture neglecting servo dynamics. Experimental results demonstrate the controller’s excellent learning and control performances compared with a conventional controller and thus has good potential for practical use in industrial robots.

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