数字孪生驱动的汇流行星排故障预测研究综述

Overview of research on fault prediction of sink pop star platoon driven by digital twin

摘要:

为了在第四次工业革命中抢占制高点，各国紧锣密鼓的进行着自己的信息化健身，数字孪生技术作为关键技术之一，可以实现物理世界与信息世界的交互，将该技术应用到装甲车辆汇流行星排的故障预测，可以实时预测车辆运行状态，有效降低了事故发生的概率，大大提高了车辆的安全性，对提高战斗力有重要意义。在综述数字孪生技术在故障预测研究方面的发展历程的基础上，针对装甲车辆汇流行星排实际工作过程中难以及时预测故障的问题，提出了四层数字孪生框架，即物理实体层，信息交互层，数据互动层和人机交互层，并阐述了每一层的具体功能要求，预期实现装甲车辆汇流行星排在发生故障前及时预警，从而达到提高设备使用寿命及驾驶安全性的目的。

Abstract:

In order to seize the commanding height in the fourth industrial revolution, all countries are vigorously carrying out their own information-based fitness. As one of the key technologies, digital twin technology can realize the interaction between the physical world and the information world. The application of this technology to the fault prediction of armored vehicle popular star platoon can predict the vehicle operation state in real time and effectively reduce the probability of accidents, It greatly improves the safety of vehicles and is of great significance to improve combat effectiveness. This paper summarizes the development of digital twin technology in fault prediction research. Aiming at the problem that it is difficult to predict the fault in time in the actual working process of armored vehicle popular star platoon, the digital twin method is adopted, and a four layer digital twin framework is proposed, which is physical entity layer, information interaction layer, data interaction layer and human-computer interaction layer, It also expounds the specific functional requirements of each layer, and is expected to realize the timely early warning of the popular star array of armored vehicles before failure, so as to improve the service life of equipment and driving safety.