A real-time intelligent multiple fault diagnostic system-Reference-Cited by-同舟云学术

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A real-time intelligent multiple fault diagnostic system

Published:2005-12-21 Issue:5 Volume:29 Page:590-597
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Bae Yong-Hwan,Lee Seok-Hee,Kim Ho-Chan,Lee Byung-Ryong,Jang Jaejin,Lee Jay

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

http://link.springer.com/content/pdf/10.1007/s00170-005-2614-0.pdf

Reference16 articles.

1. Lee J (1995) Modern computer-aided maintenance of manufacturing equipment and systems: review and perspective. Comp Ind Eng 28(4):793–811

2. Brotherton T (2000) Prognosis of faults in gas turbine engines. Aerospace Conference Proceedings 2000 IEEE 6:163–171

3. Kunze U (2003) Condition telemonitoring and diagnosis of power plants using web technology. Prog Nuclear Energy 43(1–4):129–136

4. Hardman W, Hess A, Sheaffer J (2000) A helicopter power train diagnostics and prognostics demonstration. Aerospace Conference Proceedings 2000 IEEE 6:355–366

5. Bae YH, Jang SK, Lee SH (1993) Work roll diagnosis by roll life prediction model in hot rolling process. J Korean Soc Precis Eng 10(3):69–80

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1. Diagnosis, Diagnosticability Analysis, and Test Point Design for Multiple Faults Based on Multisignal Modeling and Blind Source Separation;IEEE Transactions on Systems, Man, and Cybernetics: Systems;2020-01

2. A high-speed D-CART online fault diagnosis algorithm for rotor systems;Applied Intelligence;2019-06-21

3. A multi-fault diagnostic method based on an interleaved voltage measurement topology for series connected battery packs;Journal of Power Sources;2019-03

4. A method of searching fault propagation paths in mechatronic systems based on MPPS model;Journal of Central South University;2018-09

5. Gaussian Mixture Model Using Semisupervised Learning for Probabilistic Fault Diagnosis Under New Data Categories;IEEE Transactions on Instrumentation and Measurement;2017-04

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