|
|
Frontiers of Information Technology & Electronic Engineering
ISSN 2095-9184 (print), ISSN 2095-9230 (online)
2025 Vol.26 No.7 P.1194-1208
Data-driven intermittent connection fault diagnosis for complex topology DeviceNet based on Bayesian inference
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.
Key words: DeviceNet; Fieldbus; Complex topology; Fault diagnosis; Intermittent connection; Bayesian inference
浙江大学流体动力与机电系统国家重点实验室，中国杭州市，310027
摘要：随着工业自动化系统中DeviceNet拓扑结构越来越复杂，以及工业设备和工艺的可靠性要求越来越严格，，网络自身故障的诊断方法愈发重要。DeviceNet系统常出现间歇性连接（IC）故障，严重危害生产性能，甚至危及运行安全。然而，现有DeviceNet的IC故障诊断方法--尤其针对复杂拓扑网络的方法--无法直接处理多个故障的场景，需要人工干预才能全面诊断。针对复杂拓扑DeviceNet网络，本文提出一种基于贝叶斯推断的新型数据驱动IC故障诊断方法，该方法可在不影响系统正常运行的情况下，准确且高效地定位网络中所有IC故障。首先，通过分析被IC故障中断的数据帧定义观测症状，并通过整合观测症状与网络拓扑信息推导嫌疑IC故障。其次，利用观测症状的数量信息及其与嫌疑IC故障的因果关系，提出基于贝叶斯推断的估计方法，用来估计每个嫌疑IC故障发生于网络的后验概率。最后，开发一种基于最大似然的快速诊断算法，在不同复杂故障场景下快速识别IC故障位置。通过搭建实验台，在各种网络拓扑和故障场景下进行案例研究，验证所提方法的有效性和优势。实验结果表明，本文所提方法诊断出的IC故障位置与实验设置一致。
关键词组：
References:
Open peer comments: Debate/Discuss/Question/Opinion
<1>
DOI:
10.1631/FITEE.2400696
CLC number:
TP277
Download Full Text:
Downloaded:
770
Clicked:
608
Cited:
0
On-line Access:
2025-07-28
Received:
2024-08-08
Revision Accepted:
2024-11-11
Crosschecked:
2025-07-30