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Abstract: As the topology of deviceNet in industrial automation systems grows more complex and the reliability requirement for industrial equipment and processes becomes more stringent, the importance of network troubleshooting is increasingly evident. intermittent connection (IC) faults frequently occur in deviceNet systems, impairing production performance and even operational safety. However, existing IC troubleshooting methods for deviceNet, especially those with complex topologies, cannot directly handle multi-fault scenarios, which require human intervention for a full diagnosis. In this paper, a novel data-driven IC fault diagnosis method based on bayesian inference is proposed for deviceNet with complex topologies, which can accurately and efficiently localize all IC faults in the network without interrupting the normal system operation. First, the observation symptoms are defined by analyzing the data frames interrupted by IC faults, and the suspected IC faults are derived by integrating the observation symptoms and the network topology information. Second, a bayesian inference-based estimation approach for the posterior probability of each suspected fault occurring in the network is proposed using the quantity of observation symptoms and their causal relationships regarding the suspected faults. Finally, a maximum likelihood-based fast diagnosis algorithm is developed to rapidly identify the IC fault locations in various complex scenarios. A laboratory testbed is constructed and case studies are conducted under various topologies and fault scenarios to demonstrate the effectiveness and advantages of the proposed method. Experimental results show that the IC fault locations diagnosed by the proposed method agree well with the experimental setup.

基于贝叶斯推断的数据驱动复杂拓扑DeviceNet间歇性连接故障诊断方法

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