基于plc的工业控制系统的混合误差检测技术

Navid Rajabpour, Yasser Sedaghat
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引用次数: 3

摘要

如今,工业控制系统(ics)被用来监测和控制安全关键的工业过程。监控和数据采集(SCADA)系统是远程通信网络中用于集中监控和控制现场站点的集成控制系统。SCADA是由多个远程终端单元(rtu)和一个主终端单元(MTU)组成的分布式系统。rtu与现场传感器、本地控制设备和现场执行器接口,MTU从rtu收集数据,提供操作员界面显示信息,并控制远程站点。rtu通常通过客户端/服务器网络与MTU连接。由于rtu通常在恶劣的工业环境中运行,因此容错是一个关键要求,特别是对于安全关键型工业过程。研究表明,由于恶劣环境导致的大量暂态故障导致RTU处理器的控制流错误。提出了一种控制流检测技术,称为PLC-CFC,用于检测SCADA系统中多个rtu的控制流错误。所提出的技术可以应用于所有采用微控制器、微处理器、plc或个人计算机作为其rtu的集成电路。该技术已在一个由PLC设备和主服务器组成的实际集成电路上进行了实验验证。实验评估,在分布式系统上注入了3万个故障,PLC-CFC技术检测到的故障超过96.76%。该技术的性能开销和内存开销平均分别约为18.12%和16.17%。
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A hybrid-based error detection technique for PLC-based Industrial Control Systems
Nowadays, Industrial Control Systems (ICSs) are employed to monitor and control safety-critical industrial processes. A Supervisory Control and Data Acquisition (SCADA) system is an ICS to perform centralized monitoring and also to control field sites in long-distance communication networks. A SCADA is a distributed system composed of several Remote Terminal Units (RTUs) and a Master Terminal Unit (MTU). RTUs interface with field sensors, local control devices, and field actuators, and the MTU gathers data from RTUs, provides an operator interface to display information, and controls remote sites. RTUs are typically connected to the MTU through a client/server network. Since RTUs operate commonly in a harsh industrial environment, fault tolerance is a key requirement, especially for safety-critical industrial processes. Studies show that a significant number of transient faults caused by a harsh environment lead to control flow errors in the RTU's processors. A control flow checking technique, called PLC-CFC, has been proposed to detect control flow errors in several RTUs in a SCADA system. The proposed technique can be applied to all ICSs which employ microcontrollers, microprocessors, PLCs, or personal computers as their RTUs. The proposed technique has been experimentally evaluated on a real ICS consists of some PLC devices and a main server. For experimental evaluation, 30,000 faults were injected on distributed system and the PLC-CFC technique detected more than 96.76% of the injected faults. The performance and the memory overheads of the technique are about 18.12% and 16.17% on average, respectively.
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