JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (11): 46-57.doi: 10.6040/j.issn.1671-9352.0.2020.353

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Fault tracing method of industrial production control data network based on SDG simplified model

Yan-hua YANG1(),Li-gang YAO2   

  1. 1. College of Engineering, Fujian Jiangxia University, Fuzhou 350108, Fujian, China
    2. College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, Fujian, China
  • Received:2020-07-15 Online:2020-11-20 Published:2020-11-17

Abstract:

For the fault diagnosis and source tracing in industrial production control data network, this paper proposes a solution based on SDG simplified model. Firstly, a SDG model is established based on the structural characteristics of the industrial production control data network, then the search space for fault location is reduced through hierarchical and demarcated modeling method.On this basis, according to the observed signal amount extracted when the business alarm occurs, analyse the survival of the nodes based on the longitudinal data and survival data. Secondly, combined with the analysis of the characteristics of network faults, the single-node fault detemination method is used to determine whether each node device is faulty. Finally, based on the fault cause classification of the fault tracing database in the traceability diagnosis rules, the root cause of the network fault is determined. Through the test and verification method, the feasibility of the proposed method in the industrial production control data network is verified, which can meet the requirements of rapid fault tracing.

Key words: industrial production control data network, symbolic directed graph, fault propagation model, hierarchical and demarcated modeling, survival analysis

CLC Number: 

  • TP277

Fig.1

5-node LAN structure diagram"

Fig.2

SDG model diagram of hierarchical domain division"

Fig.3

Fault traceability analysis process diagram"

Fig.4

Fault tracing diagnosis process"

Fig.5

Sample network"

Fig.6

Hierarchical SDG model"

Table 1

Adjacency matrix"

B1 B2 B3 B4 B5 B6 B7
B1 0 1 1 1 0 0 0
B2 0 0 0 0 0 0 0
B3 0 0 0 0 0 1 0
B4 0 0 0 0 0 0 0
B5 0 1 0 1 0 0 0
B6 0 0 0 0 0 0 1
B7 0 1 0 0 0 0 0

Table 2

Reachability matrix"

B1 B2 B3 B4 B5 B6 B7
B1 0 1 1 0 0 1 0
B2 0 0 0 0 0 0 0
B3 0 0 0 0 0 1 0
B4 0 0 0 0 0 0 0
B5 0 0 0 1 0 0 0
B6 0 0 0 0 0 0 0
B7 0 0 0 0 0 0 0

Table 3

Running condition before fault nodes"

C1 B6 B3 B1 A7 A3
节点运行状态 1 1 1 1 1 1
接口运行状态 8/36 6/150 4/150 6/150 9/48 4/48

Table 4

Running condition after fault nodes"

C1 B6 B3 B1 A7 A3
节点运行状态 1 1 1 1 1 1
接口运行状态 0/36 0/150 0/150 5/150 9/48 4/48

Table 5

Survival variable of each node in the fault candidate result set"

C1 B6 B3 B1 A7 A3
0 0 0 0.833 1 1
1 ABDELLATIF S , BERTHOU P , VILLEMUR T , et al. Management of industrial communications slices: towards the application driven networking concept[J]. Computer Communications, 2020, 155 (4): 104- 116.
2 STEWART J , SCHWEINBERGER M , BOJANOWSKI M , et al. Multilevel network data facilitate statistical inference for curved ERGMs with geometrically weighted terms[J]. Social Networks, 2019, 59 (10): 98- 119.
3 HU W K , CHEN T W , SHAH S L . Detection of frequent alarm patterns in industrial alarm floods using itemset mining methods[J]. IEEE Transactions on Industrial Electronics, 2018, 65 (9): 7290- 7300.
doi: 10.1109/TIE.2018.2795573
4 GURURAJAPATHY S S , MOKHLIS H , ILLIAS H A . Fault location and detection techniques in power distribution systems with distributed generation: a review[J]. Renewable and Sustainable Energy Reviews, 2017, 74 (7): 949- 958.
5 DIDEHVAR S , CHABANLOO R M . Accurate estimating remote end equivalent impedance for adaptive one-ended fault location[J]. Electric Power Systems Research, 2019, 170 (5): 194- 204.
6 QING G , ZHANG Z M , SONG F . Ensemble of Bayesian predictors and decision trees for proactive failure management in cloud computing systems[J]. Journal of Communications, 2012, 7 (1): 52- 61.
7 刘洪波, 陈刚, 宫钦. 基于神经网络的通信网络告警关联分析及应用[J]. 电信技术, 2018, (5): 32- 35.
LIU Hongbo , CHEN Gang , GONG Qin . Alarm correlation analysis and application of communication network based on neural network[J]. Telecommunications Technology, 2018, (5): 32- 35.
8 邓蕾蕾, 张献. 基于定性仿真理论的故障检测与诊断[J]. 哈尔滨理工大学学报, 2012, 17 (5): 79- 83.
DENG Leilei , ZHANG Xian . Fault detection and diagnosis based on qualitative simulation[J]. Journal of Harbin University of Science and Technology, 2012, 17 (5): 79- 83.
9 BOBROW D G . Qualitative reasoning about physical systems: an introduction[J]. Artificial Intelligence, 1984, 24 (1/2/3): 1- 5.
10 杨斌. 基于故障树和知识库的IMS业务故障诊断技术研究应用[J]. 计算机与数字工程, 2018, 46 (6): 119- 123.
YANG Bin . Research and application of business fault diagnosis of IMS network based on fault tree and knowledge base[J]. Computer & Digital Engineering, 2018, 46 (6): 119- 123.
11 CONG X Y , FANTI M P , MANGINI A M , et al. Decentralized fault diagnosis by Petri nets and integer linear programming[J]. IFAC-Papers Online, 2017, 50 (1): 13624- 13629.
doi: 10.1016/j.ifacol.2017.08.2390
12 王勉宇.基于图论的过程故障诊断研究[D].杭州:浙江大学, 2002.
WANG Mianyu. Process fault diagnosis based on graph theory[D]. Hangzhou: Zhejiang University, 2002.
13 杨恒占, 张可, 钱富才. 基于模糊分层SDG模型的故障推理方法[J]. 计算机系统应用, 2017, 26 (4): 104- 109.
YANG Hengzhan , ZHANG Ke , QIAN Fucai . Fault reasoning method based on fuzzy hierarchical SDG model[J]. Journal of Computer & Applications, 2017, 26 (4): 104- 109.
14 田娟.基于主元分析的符号有向图故障诊断方法与应用[D].太原:太原理工大学, 2012.
TIAN Juan. The method and application of SDG fault diagnosis based on principal component analysis[D]. Taiyuan: Taiyuan University of Technology, 2012.
15 杨帆, 萧德云. SDG建模及其应用的进展[J]. 控制理论与应用, 2005, (5): 93- 100.
YANG Fan , XIAO Deyun . Review of SDG modeling and its application[J]. Control Theory &. Applications, 2005, (5): 93- 100.
16 SMAILI R , HARABI R E , ABDELKRIM M N . Design of fault monitoring framework for multi-energy systems using Signed directed graph[J]. IFAC-Papers Online, 2017, 50 (7): 15734- 15739.
17 LIU Y K , WU G H , XIE C L , et al. A fault diagnosis method based on signed directed graph and matrix for nuclear power plants[J]. Nuclear Engineering and Design, 2016, 297 (2): 166- 174.
18 JIANG Y , ZHANG H W , CHEN J . Sign-consensus of linear multi-agent systems over signed directedgraphs[J]. IEEE Transactions on Industrial Electronics, 2017, 64 (6): 5075- 5083.
doi: 10.1109/TIE.2016.2642878
19 杨蕊.基于定量知识的分层有向图故障诊断方法及其应用[D].太原:太原理工大学, 2014.
YANG Rui. Hierarchical directed graph based on quantitative knowledge fault diagnosis approach and its application[D]. Taiyuan: Taiyuan University of Technology, 2014.
20 XIE G, WANG X E, XIE K M. SDG-based fault diagnosis and application based on reasoning method of granular computing[C]//Proceedings of 2010 Chinese Control and Decision Conference. New York: IEEE Press, 2010: 1719-1723.
21 TSELYKH A , VASILEV V , TSELYKH L . Clustering method based on the elastic energy functional of directed signed weightedgraphs[J]. Physica A: Statistical Mechanics and Its Applications, 2019, 523 (6): 392- 407.
22 BURRM A , LIPMAND J . Quadratic-monomial generated domains from mixed signed, directed graphs[J]. International Journal of Algebra and Computation, 2019, 29 (2): 279- 308.
doi: 10.1142/S0218196719500024
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