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A Survey of Fault Diagnosis and Fault-Tolerant Techniques Part II: Fault Diagnosis with Knowledge-Based and Hybrid/Active Approaches
Abstract
This is the second-part paper of the survey on fault diagnosis and fault-tolerant techniques, where fault diagnosis methods and applications are overviewed, respectively, from the knowledge-based and hybrid/active viewpoints. With the aid of the first-part survey paper, the second-part review paper completes a whole overview on fault diagnosis techniques and their applications. Comments on the advantages and constraints of various diagnosis techniques, including model-based, signal-based, knowledge-based, and hybrid/active diagnosis techniques, are also given. An overlook on the future development of fault diagnosis is presented.
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Cites
Support vector machine in machine condition monitoring and fault diagnosis
Widodo, A., & Yang, B.-S. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.12.007 · Cited by 1,650
Research works printed but not linked: 59
- 10.1109/TIE.2013.2272276 · doi:10.1109/tie.2013.2272276
- 10.1016/j.na.2009.06.037 · doi:10.1016/j.na.2009.06.037
- 10.1109/TCAD.2013.2287184 · DOI
- 10.4236/jcc.2014.29004 · doi:10.4236/jcc.2014.29004
- namdari, 2014, Process fault diagnosis using support vector machines with a genetic algorithm based parameter tuning, J Autom Control, 2, 1
- 10.1155/2014/836895 · DOI
- 10.1109/TIE.2012.2229675 · DOI
- 10.1016/j.anucene.2012.06.003 · doi:10.1016/j.anucene.2012.06.003
- 10.4236/epe.2009.11002 · DOI
- 10.1109/TIE.2013.2261033 · DOI
- 10.1109/TCST.2013.2291069 · doi:10.1109/tcst.2013.2291069
- rychetsky, 0, Support vector for engine knock detection, Proc Int Joint Conf Neural Netw, 969
- tax, 0, Pump failure determination using support vector data description, Proc Lecture Notes Comput Sci, 1642, 415, 10.1007/3-540-48412-4_35 · DOI
- angeli, 2004, On-line fault detection techniques for technical systems: A survey, Int J Comput Sci Appl, 1, 12
- 10.1109/TIE.2015.2417501 · DOI · Paper page
- 10.1002/cem.2629 · DOI
- 10.1109/TCST.2013.2283925 · doi:10.1109/tcst.2013.2283925
- zhao, 2014, Fault detection of batch process based on multi-way Kernel T-PLS, J Chem Pharm Res, 6, 338
- guo, 2014, Envelope extraction based dimension reduction for independent component analysis in fault diagnosis of rolling element bearing, J Vib Control, 333, 2983
- 10.1109/TII.2012.2209663 · DOI · Paper page
- 10.1109/TII.2009.2030793 · DOI
- 10.1155/2014/213656 · doi:10.1155/2014/213656
- 10.1002/cjce.21881 · DOI
- 10.1007/BFb0009313 · doi:10.1007/bfb0009313
- 10.1109/TIE.2013.2238874 · DOI · Paper page
- 10.1109/TIE.2012.2218561 · DOI · Paper page
- 10.1109/TAC.2012.2188430 · doi:10.1109/tac.2012.2188430
- 10.1016/j.automatica.2009.04.028 · doi:10.1016/j.automatica.2009.04.028
- scott, 0, A hybrid stochastic-deterministic input design method for active fault diagnosis, Proc IEEE Conf Decision Control, 5656
- 10.1016/j.automatica.2014.03.016 · doi:10.1016/j.automatica.2014.03.016
- campbell, 2004, Auxiliary Signal Design for Failure Detection, 10.1515/9781400880041 · doi:10.1515/9781400880041
- kerestecioglu, 1993, Change Detection and Input Design in Dynamic Systems
- ma, 2012, Development of the task-based expert system for machine fault diagnosis, J Phys Conf Series, 364
- 10.4236/ijis.2012.23009 · doi:10.4236/ijis.2012.23009
- 10.1109/TIE.2007.911951 · DOI · Paper page
- 10.1016/S0098-1354(02)00161-8 · DOI · Paper page
- 10.1016/S0263-8762(07)73181-7 · DOI
- 10.1016/S1004-9541(08)60352-3 · doi:10.1016/s1004-9541(08)60352-3
- 10.1109/TCST.2007.899744 · doi:10.1109/tcst.2007.899744
- 10.1021/ie300072q · doi:10.1021/ie300072q
- jiang, 0, Rolling bearing fault diagnosis approach using probabilistic principal component analysis denoising and cyclic bispectrum, J Vib Control
- 10.1007/s00170-012-4294-x · doi:10.1007/s00170-012-4294-x
- 10.1109/TII.2012.2214394 · doi:10.1109/tii.2012.2214394
- henley, 0, Application of expert systems to fault diagnosis, Proc AIChE Annu Meet
- 10.1109/TII.2013.2243743 · DOI · Paper page
- 10.1016/j.applthermaleng.2005.04.020 · doi:10.1016/j.applthermaleng.2005.04.020
- chester, 0, Rule-based computer alarm analysis in chemical process plants, Proceedings of 7th Micro-Delcon, 22
- 10.1016/j.psep.2007.10.014 · doi:10.1016/j.psep.2007.10.014
- toffolo, 2008, Energy system diagnosis by a fuzzy expert system with genetically evolved rules, Int J Thermodyn, 11, 115
- 10.1109/TIE.2013.2263777 · DOI · Paper page
- mostafa, 2012, Implementing an expert diagnostic assistance system for car failure and malfunction, Int J Comput Sci, 9, 1
- 10.1109/TIE.2012.2192894 · DOI · Paper page
- 10.1109/TII.2012.2231870 · DOI
- 10.1109/TII.2013.2271979 · DOI
- 10.1109/TIE.2012.2230598 · DOI · Paper page
- 10.1109/TCST.2009.2020863 · DOI
- linda, 2014, FN-DFE: Fuzzy-neural data fusion engine for enhanced resilient state-awareness of hybrid energy systems, IEEE Trans Cybern, 44, 2065, 10.1109/TCYB.2014.2323891 · DOI
- wang, 2012, Fault diagnosis method based on fuzzy support vector machines and self-organizing map neural network, Int J Adv Comput Technol, 4, 139
- ozgonenel, 2011, A complete motor protection algorithm based on PCA and ANN: A real time study, Turkish J Elect Eng Comput Sci, 19, 317
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Applications of machine learning to machine fault diagnosis: A review and roadmap
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997
A new convolutional neural network-based data-driven fault diagnosis method
Wen, L., Li, X., Gao, L., & Zhang, Y. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2774777 · Cited by 2,222
Bayesian Networks in Fault Diagnosis
Cai, B., Huang, L., & Xie, M. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2695583 · Cited by 474
Recent advances in sensor fault diagnosis: a review
Li, D., Wang, Y., Wang, J., Wang, C., & Duan, Y. (2020) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2020.111990 · Cited by 323
Actuator and sensor faults estimation based on proportional integral observer for TS fuzzy model
Youssef, T., Chadli, M., Karimi, H. R., & Wang, R. (2017) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2016.09.020 · Cited by 266
Metadata
- Authors
- Gao, Z., Cecati, C., & Ding, S.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2015
- Pages
- 1-1
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2100028154
- Cited by
- 776
- References
- 1
- FWCI
- 59.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gao, Z., Cecati, C., & Ding, S. (2015). A Survey of Fault Diagnosis and Fault-Tolerant Techniques Part II: Fault Diagnosis with Knowledge-Based and Hybrid/Active Approaches. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2015.2419013
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