广东工业大学学报 ›› 2020, Vol. 37 ›› Issue (04): 15-20.doi: 10.12052/gdutxb.200052

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基于SDG的集群打印系统故障智能诊断研究

谢光强, 陈俊宇, 郭小全   

  1. 广东工业大学 计算机学院，广东 广州 510006
  • 收稿日期:2020-03-19 出版日期:2020-07-11 发布日期:2020-07-11
  • 作者简介:谢光强(1979-)，男，教授，博士，主要研究方向为多智能体、智能控制
  • 基金资助:
    国家自然科学基金资助项目(61876043，61472089)；NSFC-广东联合基金资助项目(U1501254)

A Research on Intelligent Fault Diagnosis of Cluster Printing System Based on SDG

Xie Guang-qiang, Chen Jun-yu, Guo Xiao-quan   

  1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2020-03-19 Online:2020-07-11 Published:2020-07-11

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摘要： 随着网络购物的发展, 订单打印需求增大, 集群打印系统能够有效地提高效率。但集群系统需要较高的鲁棒性和可靠性, 因此订单监控和处理打印设备故障成为集群打印系统的核心问题。通过将具有实时监控节点数据和揭示故障传播路径特点的符号有向图(Signed Directed Graph, SDG)技术应用到集群打印系统, 建立了集群打印系统故障诊断推理规则, 并形成“If-Then”形式的诊断规则库。此外, 构建了订单全生命追踪模型, 结合诊断规则库对故障任务进行识别和管理, 实现了集群打印系统的故障任务转移和自恢复。

关键词: 智能诊断, 符号有向图, 全生命追踪, 诊断规则库

Abstract: With the development of online shopping, the demand for order printing has increased, and the cluster printing system can effectively improve efficiency. However, the cluster system requires high robustness and reliability, so order monitoring and handling of printing equipment failures have become the core issues of the cluster printing system. The SDG technology with the characteristics of real-time monitoring node data and revealing the fault propagation path is applied to the cluster printing system. The fault diagnosis reasoning rules of the cluster printing system are established, and the “If-Then” form of diagnosis rule base is formed. In addition, an order-full-life tracking model is constructed, and fault tasks are identified and managed in combination with a diagnostic rule base to implement fault task transfer and self-recovery of the cluster printing system.

Key words: intelligent diagnosis, Signed Directed Graph(SDG), life tracking, diagnostic rule base

中图分类号: 

  • TN915.04
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