Skip to main content
SpringerLink
Log in

Data driven CAN node reliability assessment for manufacturing system

  • Published:
Chinese Journal of Mechanical Engineering Submit manuscript

Abstract

The reliability of the Controller Area Network(CAN) is critical to the performance and safety of the system. However, direct bus-off time assessment tools are lacking in practice due to inaccessibility of the node information and the complexity of the node interactions upon errors. In order to measure the mean time to bus-off(MTTB) of all the nodes, a novel data driven node bus-off time assessment method for CAN network is proposed by directly using network error information. First, the corresponding network error event sequence for each node is constructed using multiple-layer network error information. Then, the generalized zero inflated Poisson process(GZIP) model is established for each node based on the error event sequence. Finally, the stochastic model is constructed to predict the MTTB of the node. The accelerated case studies with different error injection rates are conducted on a laboratory network to demonstrate the proposed method, where the network errors are generated by a computer controlled error injection system. Experiment results show that the MTTB of nodes predicted by the proposed method agree well with observations in the case studies. The proposed data driven node time to bus-off assessment method for CAN networks can successfully predict the MTTB of nodes by directly using network error event data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. YANG Z, KAN Y, CHEN F, et al. Bayesian reliability modeling and assessment solution for NC machine tools under small-sample data[J]. Chinese Journal of Mechanical Engineering, 2015, 28(6): 1229–1239.

    Article  Google Scholar 

  2. WANG Y, ZHANG F, CUI T, et al. Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer[J]. Chinese Journal of Mechanical Engineering, 2016, 29(2): 386–395.

    Article  Google Scholar 

  3. GAUJAL B, NAVET N. Fault confinement mechanisms on CAN: analysis and improvements[J]. IEEE Transactions on Vehicular Technology, 2005, 54(3): 1103–1113.

    Article  Google Scholar 

  4. CHEN J, LUO F, SUN Z. Reliability analysis of CAN nodes under electromagnetic interference[C]//IEEE International Conference on Vehicular Electronics and Safety, Beijing, China, Dec 13–15, 2006: 367–371.

    Google Scholar 

  5. LEI Y, DJURDJANOVIC D, NI J. DeviceNet reliability assessment using physical and data link layer parameters[J]. Quality and Reliability Engineering International, 2010, 26(7): 703–715.

    Article  Google Scholar 

  6. CAUFFRIEZ L, CONRARD B, THIRIET J M, et al. Fieldbuses and their influence on dependability[C]//IEEE IMTC 2003 Instrumentation and Measurement Technology Conference. VAIL, CO, United States, May 20–22, 2003, 2: 1005–1008.

    Google Scholar 

  7. JUMEL F, THIRIET J M, AUBRY J F, et al. Toward an Information-Based Approach for the Dependability Evaluation of Distributed Control Systems[C]//IEEE Instrumentation and Measurement Technology Conference, VAIL, CO, United States, May 20–22, 2003, 1: 270–275.

    Google Scholar 

  8. CORNO F, PEREZ J, RAMASSO M, et al. Validation of the dependability of CAN-based networked systems[C]//Proceedings of the Ninth IEEE International High-Level Design Validation and Test Workshop, Sonoma Valley, CA, USA, Nov 10–12, 2004: 161–164.

    Chapter  Google Scholar 

  9. HANSSON H A, NOLTE T, NORSTRÖ M C, et al. Integrating reliability and timing analysis of CAN-based systems[J]. IEEE Transactions on Industrial Electronics, 2002, 49(6): 1240–1250.

    Article  Google Scholar 

  10. GUJARATI A, AGGARWAL A, CLEMENT A, et al. Probably Right, Probably on Time: An Analysis of CAN in the Presence of Host and Network Faults [C]//21st IEEE Real-Time and Embedded Technology and Applications Symposium, Seattle, USA, April 13–17, 2015: 9–10.

    Google Scholar 

  11. TRAN E, KOOPMAN P. Multi-bit error vulnerabilities in the controller area network protocol[M] Pittsburgh: Carnegie Mellon University Press, 1999.

    Google Scholar 

  12. RODRÍGUEZ-NAVAS G, JIMENEZ J, PROENZA J. An architecture for physical injection of complex fault scenarios in CAN networks[C]//IEEE Conference on Emerging Technologies and Factory Automation, Lisbon, Portugal, Sep 16–19, 2003, 2: 125–128.

    Google Scholar 

  13. REORDA M S, VIOLANTE M. On-line analysis and perturbation of CAN networks[C]//19th IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems, Cannes, France, Oct 10–13, 2004: 424–432.

    Google Scholar 

  14. SHORT M, SHEIKH I, ALEY S, et al. Bandwidth-efficient burst error tolerance in TDMA-based CAN networks[C]//IEEE 16th Conference on Emerging Technologies & Factory Automation, Toulouse, France, Sep 5–9, 2011: 1–8.

    Google Scholar 

  15. CHEN H, TIAN J. Research on the controller area network[C]//IEEE International Conference on Networking and Digital Society, Guiyang, China, May 30–31, 2009, 2: 251–254.

    Google Scholar 

  16. CENA G, BERTOLOTTI I C, HU T, et al. Software-based assessment of the synchronization and error handling behavior of a real CAN controller[C]//IEEE 18th Conference on Emerging Technologies & Factory Automation, Cagliari, Italy, Sep 10–13, 2013: 1–9.

    Chapter  Google Scholar 

  17. BARRANCO M, PROENZA J, RODRÍ GUEZ-NAVAS G, et al. An active star topology for improving fault confinement in CAN networks[J]. IEEE Transactions on Industrial Informatics, 2006, 2(2): 78–85.

    Article  Google Scholar 

  18. YARAMASU A, CAO Y, LIU G, et al. Intermittent wiring fault detection and diagnosis for SSPC based aircraft power distribution system[C]//IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Kaohsiung, Taiwan, China, Jul 11–14, 2012: 1117–1122.

    Google Scholar 

  19. BARRANCO M, PROENZA J. Towards understanding the sensitivity of the reliability achievable by simplex and replicated star topologies in CAN[C]//IEEE 16th Conference on Emerging Technologies & Factory Automation, Toulouse, France, Sep 5–9, 2011: 1–4.

    Google Scholar 

  20. BARRANCO M, PROENZA J, ALMEIDA L. Quantitative comparison of the error-containment capabilities of a bus and a star topology in CAN networks[J]. IEEE Transactions on Industrial Electronics, 2011, 58(3): 802–813.

    Article  Google Scholar 

  21. PRODANOV W, VALLE M, BUZAS R. A controller area network bus transceiver behavioral model for network design and simulation[J]. IEEE Transactions on Industrial Electronics, 2009, 56(9): 3762–3771.

    Article  Google Scholar 

  22. ZHAO J, LEI Y. Modeling for early fault detection of intermittent connections on controller area networks[C]//IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Kaohsiung, Taiwan, China, Jul 11–14, 2012: 1135–1140.

    Google Scholar 

  23. LEI Y, YUAN Y, ZHAO J. Model-based detection and monitoring of the intermittent connections for CAN networks[J]. IEEE Transactions on Industrial Electronics, 2014, 61(6): 2912–2921.

    Article  Google Scholar 

  24. LEI Y, YUAN Y, SUN Y. Fault location identification for localized intermittent connection problems on CAN networks[J]. Chinese Journal of Mechanical Engineering, 2014, 27(5): 1038–1046.

    Article  Google Scholar 

  25. FERREIRA J, OLIVEIRA A, FONSECA P, et al. An experiment to assess bit error rate in CAN[C]//Proceedings of 3rd International Workshop of Real-Time Networks (RTN2004), Catania, Italy, Jun 29, 2004: 15–18.

    Google Scholar 

  26. LEI Y, DJURDJANOVIC D. Diagnosis of intermittent connections for DeviceNet[J]. Chinese Journal of Mechanical Engineering, 2010, 23(5): 606–612.

    Article  Google Scholar 

  27. CHEN N, ZHOU S, CHANG T S, et al. Attribute control charts using generalized zero-inflated Poisson distribution[J]. Quality and Reliability Engineering International, 2008, 24(7): 793–806.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China

    Leiming Zhang, Yong Yuan & Yong Lei

  2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China

    Yong Lei

Authors
  1. Leiming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Lei.

Additional information

Supported by National Natural Science Foundation of China(Grant No. 51475422), Science Fund for Creative Research Groups of National Natural Science Foundation of China(Grant No. 51521064), National Basic Research Program of China(973 Program, Grant No. 2013CB-035405), and Open Foundation of State Key Laboratory of Automotive Safety and Energy, Tsinghua University, China(Grant No. KF13011)

ZHANG Leiming, is currently a PhD candidate at State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, China. He received his BS degree in mechanical engineering from Harbin Institute of Technology, China.

YUAN Yong, received his MS degree in mechanical engineering from Zhejiang University, China.

LEI Yong, born in 1976, is an associate professor at State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, China. He received his BS degree in control science and engineering from Huazhong University of Science and Technology, China, his MS degree in manufacturing and automation from Tsinghua University, China, and his PhD degree in mechanical engineering from University of Michigan, Ann Arbor, USA. His research interests include monitoring and fault diagnosis of the networked automation systems, statistical quality control, and surgical robots.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Yuan, Y. & Lei, Y. Data driven CAN node reliability assessment for manufacturing system. Chin. J. Mech. Eng. 30, 190–199 (2017). https://doi.org/10.3901/CJME.2016.1021.124

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3901/CJME.2016.1021.124

Keywords

Search

Navigation