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

[1]Cai BP, Huang L, Xie M, 2017. Bayesian networks in fault diagnosis. IEEE Trans Ind Inform, 13(5):2227-2240.

[2]Cheng S, Peng HN, Yang C, et al., 2024a. Chassis global dynamics optimization for automated vehicles: a multiactuator integrated control method. IEEE Trans Syst Man Cybern Syst, 54(1):578-587.

[3]Cheng S, Peng HN, Dong XX, et al., 2024b. Chassis global dynamics-oriented trajectory planning for automated vehicles. IEEE Trans Syst Man Cybern Syst, 54(2):950-959.

[4]Chu KY, Liu R, Duan GJ, 2023. A gray correlation based Bayesian network model for fault source diagnosis of multistage process—small sample manufacturing system. Adv Eng Inform, 56: 101918.

[5]Gessner D, Barranco M, Ballesteros A, et al., 2014. sfiCAN: a star-based physical fault-injection infrastructure for CAN networks. IEEE Trans Veh Technol, 63(3):1335-1349.

[6]Jiang XJ, Zhou W, Hou J, 2023. Construction of fault diagnosis system for control rod drive mechanism based on knowledge graph and Bayesian inference. Nucl Sci Techn, 34:21.

[7]Lei Y, Yuan Y, Sun YC, 2014a. Fault location identification for localized intermittent connection problems on CAN networks. Chin J Mech Eng, 27(5):1038-1046.

[8]Lei Y, Yuan Y, Zhao JZ, 2014b. Model-based detection and monitoring of the intermittent connections for CAN networks. IEEE Trans Ind Electron, 61(6):2912-2921.

[9]Lei Y, Xie HB, Yuan Y, et al., 2015. Fault location for the intermittent connection problems on CAN networks. IEEE Trans Ind Electron, 62(11):7203-7213.

[10]Liu BY, Bi XW, Gu LJ, et al., 2022. Application of a Bayesian network based on multi-source information fusion in the fault diagnosis of a radar receiver. Sensors, 22(17):6396.

[11]Liu ZK, Lv KL, Zheng C, et al., 2022. A fault diagnosis method for rolling element bearings based on ICEEMDAN and Bayesian network. J Mech Sci Technol, 36(5):2201-2212.

[12]Nguyen TN, Vilim RB, 2023. Direct Bayesian inference for fault severity assessment in digital-twin-based fault diagnosis. Ann Nucl Energy, 194: 109932.

[13]Open DeviceNet Vendor Association, 2021. Technology Overview Series: DeviceNet.

[14]Wang LK, Zhang LM, Lei Y, 2023. Diagnosis of intermittent connection faults for CAN networks with complex topology. IEEE Access, 11:52199-52213.

[15]Wang ZW, Wang L, Tan YY, et al., 2021. Fault diagnosis using fused reference model and Bayesian network for building energy systems. J Build Eng, 34: 101957.

[16]Yang C, Cai BP, Zhang R, et al., 2023a. Cross-validation enhanced digital twin driven fault diagnosis methodology for minor faults of subsea production control system. Mech Syst Signal Process, 204: 110813.

[17]Yang C, Cai BP, Wu QB, et al., 2023b. Digital twin-driven fault diagnosis method for composite faults by combining virtual and real data. J Ind Inform Integr, 33: 100469.

[18]Yang WT, Reis MS, Borodin V, et al., 2022. An interpretable unsupervised Bayesian network model for fault detection and diagnosis. Contr Eng Pract, 127: 105304.

[19]Zhang LM, Lei Y, Chang Q, 2017. Intermittent connection fault diagnosis for CAN using data link layer information. IEEE Trans Ind Electron, 64(3):2286-2295.

[20]Zhang LM, Yang F, Lei Y, 2019. Tree-based intermittent connection fault diagnosis for controller area network. IEEE Trans Veh Technol, 68(9):9151-9161.

[21]Zhao JZ, Lei Y, 2012. Modeling for early fault detection of intermittent connections on controller area networks. IEEE/ASME Int Conf on Advanced Intelligent Mechatronics, p.1135-1140.