Development of a Design Methodology for Cloud Distributed Control Systems of Mobile Robots

This article addresses the problem of cloud distributed control systems development for mobile robots. The authors emphasize the lack of a design methodology to guide the process of the development in accordance with specific technical and economic requirements for the robot. On the analysis of various robots architectures, the set of the nine most significant parameters are identified to direct the development stage by stage. Based on those parameters, the design methodology is proposed to build a scalable three-level cloud distributed control system for a robot. The application of the methodology is demonstrated on the example of AnyWalker open source robotics platform. The developed methodology is also applied to two other walking robots illustrated in the article.

Fault detection Automation in Distributed Control Systems using Data-driven methods: SVM and KNN

<p>Fault diagnostic methods with fuzzy logic methods, SVM, KNN and artificial intelligence systems have been used in complex systems such as wind turbines, gas turbines, power distribution systems, power transformers and rotary machines, but in the specific field of distributed control systems, the vacancy of this topic is strongly felt. Due to the need of the industry to detect faults quickly and in a timely manner in all modes of sensors, actuators, outputs and control logics to maintain expensive, valuable resources, important and complex equipment, it is very necessary to enter this topic. In this paper, a suitable theoretical and practical basis for diagnosing various types of faults in the DCS of a gas refinery is done. The fact that the operator quickly identifies the area and the cause of the fault can avoid huge losses in terms of downtime. Automation of fault diagnosis in DCS has not been explicitly mentioned in any article or book, and here the plan is presented for the first time. In this design, we connect MATLAB classification apps to the industrial system like DCS, then data are analyzed by SVM and KNN methods to detect faults. The results show that faults can be detected with a probability of more than 85% accuracy without the need for on-site expert force and with much less time.</p>

Fault detection Automation in Distributed Control Systems using Data-driven methods: SVM and KNN

<p>Fault diagnostic methods with fuzzy logic methods, SVM, KNN and artificial intelligence systems have been used in complex systems such as wind turbines, gas turbines, power distribution systems, power transformers and rotary machines, but in the specific field of distributed control systems, the vacancy of this topic is strongly felt. Due to the need of the industry to detect faults quickly and in a timely manner in all modes of sensors, actuators, outputs and control logics to maintain expensive, valuable resources, important and complex equipment, it is very necessary to enter this topic. In this paper, a suitable theoretical and practical basis for diagnosing various types of faults in the DCS of a gas refinery is done. The fact that the operator quickly identifies the area and the cause of the fault can avoid huge losses in terms of downtime. Automation of fault diagnosis in DCS has not been explicitly mentioned in any article or book, and here the plan is presented for the first time. In this design, we connect MATLAB classification apps to the industrial system like DCS, then data are analyzed by SVM and KNN methods to detect faults. The results show that faults can be detected with a probability of more than 85% accuracy without the need for on-site expert force and with much less time.</p>

Fault detection Automation in Distributed Control Systems using Data-driven methods: SVM and KNN

<p>Fault diagnostic methods with fuzzy logic methods, SVM, KNN and artificial intelligence systems have been used in complex systems such as wind turbines, gas turbines, power distribution systems, power transformers and rotary machines, but in the specific field of distributed control systems, the vacancy of this topic is strongly felt. Due to the need of the industry to detect faults quickly and in a timely manner in all modes of sensors, actuators, outputs and control logics to maintain expensive, valuable resources, important and complex equipment, it is very necessary to enter this topic. In this paper, a suitable theoretical and practical basis for diagnosing various types of faults in the DCS of a gas refinery is done. The fact that the operator quickly identifies the area and the cause of the fault can avoid huge losses in terms of downtime. Automation of fault diagnosis in DCS has not been explicitly mentioned in any article or book, and here the plan is presented for the first time. In this design, we connect MATLAB classification apps to the industrial system like DCS, then data are analyzed by SVM and KNN methods to detect faults. The results show that faults can be detected with a probability of more than 85% accuracy without the need for on-site expert force and with much less time.</p>