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

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

更新时间：2026-05-27

- 基于SDG的集群打印系统故障智能诊断研究
- A Research on Intelligent Fault Diagnosis of Cluster Printing System Based on SDG
- 广东工业大学学报 2020年37卷第4期页码：15-20
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 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
- 基金信息：
  
  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)
- DOI：10.12052/gdutxb.200052
  中图分类号： TN915.04
- 网络首发：2020-07-11，
  
  纸质出版：2020
- 稿件说明：
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谢光强, 陈俊宇, 郭小全. 基于SDG的集群打印系统故障智能诊断研究[J]. 广东工业大学学报, 2020, 37(4): 15-20.

Xie Guang-qiang, Chen Jun-yu, Guo Xiao-quan. A Research on Intelligent Fault Diagnosis of Cluster Printing System Based on SDG[J]. 2020, 37(4): 15-20.
谢光强, 陈俊宇, 郭小全. 基于SDG的集群打印系统故障智能诊断研究[J]. 广东工业大学学报, 2020, 37(4): 15-20. DOI： 10.12052/gdutxb.200052.

Xie Guang-qiang, Chen Jun-yu, Guo Xiao-quan. A Research on Intelligent Fault Diagnosis of Cluster Printing System Based on SDG[J]. 2020, 37(4): 15-20. DOI： 10.12052/gdutxb.200052.

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。

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.

关键词

Keywords

references

YANG D H, LEI J Y, YAO H. Research on metal structure fault diagnosis of lifting equipment based on intelligent control [J]. Agricultural Equipment and Vehicle Engineering, 2020,58(2): 123-126.

MAO B, YANG S, LI Y S. Improved dynamic causality diagram and fuzzy inference fusion fault diagnosis method [J]. CEA, 2020, 56(3): 259-265.

ZHANG B, YAN J, TIAN C. Study on fault diagnosis system of diesel engine fuel injection system based on BP neural network[C]// ISECS International Colloquium on Computing, Communication, Control and Management.[S.l]: IEEE Computer Society, 2008.

LI C, ZHANG S H, JOSÉ V O. Fault diagnosis for 3D printers using suboptimal networked deep learning [J]. Journal of Mechanical Engineering, 2019, 55(7): 73-80.

LEI Y, YANG B, JIANG X, et al . Applications of machine learning to machine fault diagnosis: a review and roadmap [J ] . Mechanical Systems and Signal Processing, 2020, 138: 106587.

LI Y, ZENG Y, LIU T, et al . Simultaneously learning affinity matrix and data representations for machine fault diagnosis [J ] . Neural Networks, 2020, 122: 395-406.

WANG F, PAZILA M H, ZHANG B W. Research on multiple fault diagnosis method based on signed bond graph [J]. Modern Electronic Technology, 2018, 41(16): 140-143.

XIE G Q, GUO X Q, LI Y, et al . Review of fault diagnosis methods for micro-embedded system [J ] . Application Research of Computers, 2019, 36(5): 1281-1285.

SMAILI R, El H R, ABDELKRIM M N. Fault diagnosis based on graphical tools for multi-energy processes [J]. IFAC Proceedings Volumes, 2014, 47(3): 7073-7078.

吉世明. 基于半定量SDG模型的化工过程故障诊断[D]. 广州: 华南理工大学, 2012.

VEDAM H, VENKATASUBRAMANIAN V. PCA-SDG based process monitoring and fault diagnosis [J]. Control Engineering Practice, 1999, 7(7): 903-917.

ZHANG Z Q, WU C G, ZHANG B K, et al . SDG multiple fault diagnosis by real-time inverse inference [J ] . Reliability Engineering System Safety, 2005, 87(2): 173-189.

TARIFA E E, SCENNA N J. Fault diagnosis for a MSF using a SDG and fuzzy logic [J]. Desalination, 2003, 152(1): 207-214.

LI X X, JI S M. Chemical process fault diagnosis using semi-quantitative SDG model [J]. Journal of Tsinghua University (Science and Technology), 2012, 52(8): 1112-1115.

VENKATASUBRAMANIAN V, RENGASWAMY R, KAVURI S N. A review of process fault detection and diagnosis,Part II: qualitative models and search strategies [J]. Computers Chemical Engineering, 2003, 27(3): 313-326.

VENKAT V, RAGHUNATHAN R, KEWEN Y, et al . A review of process fault detection and diagnosis, Part I: quantitative model-based methods [J ] . Computers Chemical Engineering, 2003, 27(3): 293-311.

PEDRAM M. Power optimization and management in embedded systems[C]//Asia and South Pacific Design Automation Conference, 2001. Yokohama, Japan: IEEE, 2001: 239-244.

TU G, YANG F M, HU G R. Overview of embedded operating systems [J]. Journal of Computer Applications, 2000(11): 4-9.

徐超群. 高可用集群系统关键技术的研究与实现[D].南京: 南京航空航天大学, 2014.

YANG H Z, ZANG K, QIAN F C. Fault reasoning method based on fuzzy hierarchical SDG model [J]. Application of Computer Systems, 2017, 26(4): 104-109.

LIU H G, WU J J, CHEN X Q, et al . Study on the SDG- based intelligent fault diagnosis method [J ] . System Engineering and Electronics, 2002, 24(1): 103-104.

WANG B, CHEN J D. A design method for power failure protection of embedded system [J]. Industrial Control Computer, 2005(9): 58-59.

WENG H Y, YU J M, YE D P. Design and implementation of a single chip power-down protection system based on LM358 [J]. Electronic World, 2013(4): 3.

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