Method for resilience forecasting of the calaud-oriented cyberphysical systems

Abstract

Cyberphysical system is the integration of physical space (equipment, devices and people) with computing, communication and control systems (cyberspace). The National Institute of Standards and Terminology (NIST) has defined CFS as cyberphysical systems (CFS), which are designed systems built on the continuous integration of computational algorithms and physical components. Improving the CFS provides greater opportunities for performance, adaptability, scalability, stability, security and usability, far exceeding today's simple embedded systems. CFS technology will transform the interaction of people with the engineering system [1, 2]. Today, cyberphysical systems exist in various fields, such as automotive, aerospace, civil, railway, medical. Large productions seek to increase the availability of the asset, while reducing maintenance costs through cyberphysical systems. With the development of the industry and the use of systems in production, the proposed maintenance is used constantly to avoid failures. CFS maintenance is performed when certain indicators signal that the condition of the system has deteriorated. One way to solve this problem is to provide systems with resistance properties. Such systems are able to recover quickly and continue to function in changing conditions [3-9]. Therefore, the urgent task is to develop approaches that will predict the resilience of cyberphysical systems based on cloud computing. It is necessary to build methods and tools that will monitor the condition of the CFS and predict the timely replacement of their components that may fail. The application of the process of predicting the resilience of cyberphysical systems using cloud-oriented increases the effective reliability and availability of cyberphysical systems in its life cycle by identifying future failures and reducing unscheduled maintenance. The forecasting process involves the assessment of the useful life, and the implementation of a post-forecast decision on maintenance measures in accordance with the rules. The method of predicting the resilience of cloud-oriented cyberphysical systems allows to determine the state of the cyberphysical systems using cloud computing. The mechanism used to reduce the cost of maintenance and detailed planning of maintenance operations, the apparatus of genetic algorithms.