Open Access
Volume 41, Number 3, June 2023
Page(s) 546 - 556
Published online 01 August 2023
  1. ZHONG M, XUE T, DING S X. A survey on model-based fault diagnosis for linear discrete time-varying systems[J]. Neurocomputing, 2018, 306: 51–60. [Article] [CrossRef] [Google Scholar]
  2. PAN M, ZHENG D, LAI X, et al. State estimation based fault analysis and diagnosis in a receiving-end transmission system[C]//2022 IEEE IAS Global Conference on Emerging Technologies, 2022: 1107–1112 [CrossRef] [Google Scholar]
  3. MD A, FAISAL K, AHMAD I S, et al. A bibliometric review and analysis of data-driven fault detection and diagnosis methods for process systems[J]. Industrial & Engineering Chemistry Research, 2018, 57(32): 10719–10735 [CrossRef] [Google Scholar]
  4. PULIDO B, ZAMARRENO J M, MERINO A, et al. State space neural networks and model-decomposition methods for fault diagnosis of complex industrial systems[J]. Engineering Applications of Artificial Intelligence, 2019, 79: 67–86. [Article] [CrossRef] [Google Scholar]
  5. PU C, ZHOU F, LI L. Fault diagnosis method based on recursive federated transfer learning under multi rate sampling[C]//2021 China Automation Congress, 2021: 6502–6507 [CrossRef] [Google Scholar]
  6. DAI J, TANG J, HUANG S, et al. Signal-based intelligent hydraulic fault diagnosis methods: review and prospects[J]. Chinese Journal of Mechanical Engineering, 2019, 32(5): 22 [CrossRef] [Google Scholar]
  7. ZHANG M, SU B, ZHAO L, et al. User information intrusion prediction method based on empirical mode decomposition and spectrum feature detection[J]. International Journal of Information and Communication Technology, 2020, 16(2): 99. [Article] [Google Scholar]
  8. SHANG J, ZHOU D, CHEN M, et al. Incipient sensor fault diagnosis in multimode processes using conditionally independent Bayesian learning based recursive transformed component statistical analysis[J]. Journal of Process Control, 2019, 77: 7–19. [Article] [CrossRef] [Google Scholar]
  9. LAHDHIRI H, SAID M, ABDELLAFOU K B, et al. Supervised process monitoring and fault diagnosis based on machine learning methods[J]. The International Journal of Advanced Manufacturing Technology, 2019, 102(5/6/7/8): 2321–2337. [Article] [CrossRef] [Google Scholar]
  10. CHI Y, DONG Y, WANG J, et al. Knowledge-based fault diagnosis in industrial Internet of Things: A survey[J]. IEEE Internet of Things Journal, 2022, 9(15): 12886–12900. [Article] [CrossRef] [Google Scholar]
  11. TIDRIRI K, CHATTI N, VERRON S, et al. Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: a review of researches and future challenges[J]. Annual Review in Control, 2016, 42: 65–81. [Article] [CrossRef] [Google Scholar]
  12. CHATTERJEE B, MITRA S, LAHA R, et al. Fault diagnosis in vehicular networks using do-calculus[C]//2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference, 2019 [Google Scholar]
  13. BONDORF S, NIKOLAUS P, SCHMITT J B. Catching corner cases in network calculus-flow segregation can improve accuracy[C]//International Conference on Measurement, Springer, Cham, 2018 [Google Scholar]
  14. ZHANG Ziyou, ZHAO Qianchuan, YANG Wen. Network faults detection based on network calculus[J]. Control Theory & Applications, 2019, 36(11): 1861–1870. [Article] (in Chinese) [Google Scholar]
  15. ZHANG D, WANG J. Analysis on intelligent fault diagnosis technology of industrial control network[C]//2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, 2021: 384–388 [Google Scholar]
  16. HARLI E. Pemilihan network monitoring system berdasarkan kajian efektifitas sistem informasi dengan pendekatan AHP: Studi Kasus pada "PT. TUV"[J]. Jurnal Edukasi dan Penelitian Informatika, 2016, 2(1): 64–70 [Google Scholar]
  17. DESAI V. TCP/IP network management: a case study[M]. New York: Auerbach Publications, 2020: 209–218 [Google Scholar]
  18. LIU Z. FDM: a network fault diagnosis model based on knowledge and reasoning[C]//2007 International Symposium on Communications and Information Technologies, 2007: 785–789 [CrossRef] [Google Scholar]
  19. SUN Y, ZHANG S, MIAO C, et al. Improved BP neural network for transformer fault diagnosis[J]. Journal of China University of Mining and Technology, 2007, 17(1): 138–142. [Article] [CrossRef] [Google Scholar]
  20. DU Jiang, TONG Zhihua. Problems to be solved in network management using Tivoli Netview[J]. The Agricultural Development and Finance, 2003(10): 41–42. [Article] (in Chinese) [Google Scholar]
  21. SLABICKI M, GROCHLA K. Performance evaluation of CoAP, SNMP and NETCONF protocols in fog computing architecture[C]//2016 IEEE/IFIP Network Operations and Management Symposium, 2016: 1315–1319 [CrossRef] [Google Scholar]
  22. WANG Xiuli, WANG Haiying. Analysis of the Technical Architecture of SiteView NNM[J]. China Science and Technology Review, 2009(26): 320–320. [Article] (in Chinese) [Google Scholar]
  23. WANG Zhuqing, XIAO Limin, HU Yuqi. Design and Implementation of AFDX network system monitoring scheme[J]. Computer Measurement & Control, 2018, 26(7): 62–65. [Article] (in Chinese) [Google Scholar]
  24. JING Wenjun, LI Jiandong, CUI Jie. Research and realization of monitoring and communication mechanism for avionics system based on AFDX bus[C]//Civil Avionics International Forum 2016, 2016: 202–207 (in Chinese) [Google Scholar]
  25. CHEN Wenhao, GUO Ziyan, WANG Li, et al. Research on integrated fault diagnostic of complex avionic system[J]. Computer Measurement & Control, 2016, 24(111): 1–4. [Article] (in Chinese) [Google Scholar]
  26. SONG Liqiong, SONG Dong, LI Jingwei. Integrated fault diagnostic method research of airborne equipment based on multi-signal model[J]. Computer Measurement & Control, 2014, 22(4): 975–978. [Article] (in Chinese) [Google Scholar]
  27. HU Liang, Zhang Yao. Airborne module BIT design and fault diagnosis system construction based on PHM[J]. Electro-Optic Technology Application, 2018, 33(2): 73–78. [Article] (in Chinese) [Google Scholar]
  28. ZHANG Lianming, CHEN Zhigang, HUANG Guosheng. A survey on theory and application of network calculus[J]. Computer Engineering and Applications, 2006, 42(27): 5. [Article] (in Chinese) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.