Availability evaluation of controller area networks under the influence of intermittent connection faults

Abstract

Controller area networks (CANs), as one of the widely used fieldbuses in the industry, have been extended to the automation field with strict standards for safety and reliability. In practice, factors such as fatigue and insulation wear of the cables can cause intermittent connection (IC) faults to occur frequently in the CAN, which will affect the dynamic behavior and the safety of the system. Hence, quantitatively evaluating the performance of the CAN under the influence of IC faults is crucial to real-time health monitoring of the system. In this paper, a novel methodology is proposed for real-time quantitative evaluation of CAN availability when considering IC faults, with the system availability parameter being calculated based on the network state transition model. First, the causal relationship between IC fault and network error response is constructed, based on which the IC fault arrival rate is estimated. Second, the states of the network considering IC faults are analyzed, and the deterministic and stochastic Petri net (DSPN) model is applied to describe the transition relationship of the states. Then, the parameters of the DSPN model are determined and the availability of the system is calculated based on the probability distribution and physical meaning of markings in the DSPN model. A testbed is constructed and case studies are conducted to verify the proposed methodology under various experimental setups. Experimental results show that the estimation results obtained using the proposed method agree well with the actual values.

摘要

控制器局域网(CAN)作为工业中广泛使用的现场总线之一, 已经扩展到对安全性和可靠性有严格要求的自动化领域。在实际应用中, 电缆的疲劳和绝缘磨损等因素会导致CAN总线中间歇性连接(IC)故障频繁发生, 从而影响系统的动态行为和安全。因此, 定量评估CAN在IC故障影响下的性能对系统的实时健康监测至关重要。本文提出一种考虑IC故障的CAN可用性实时定量评估方法, 该方法基于网络状态转移模型计算系统可用性参数。首先, 构建IC故障与网络错误响应之间的因果关系, 在此基础上估计IC故障到达速率。其次, 对考虑IC故障的网络状态进行分析, 采用确定与随机Petri网(DSPN)模型描述状态间的转移关系。然后, 根据DSPN模型中标识的概率分布和物理意义, 确定DSPN模型的参数并计算系统的可用度。搭建了实验平台, 并在多种实验条件下对所提方法进行实例验证。实验结果表明, 所提方法的估计结果与实际值吻合良好。