Qizhi Wang, and Xiaoxia Wang

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  1. [1] L.M. Elshenawy and T.A. Mahmoud, Fault diagnosis of timevarying processes using modified reconstruction-based contributions, Journal of Process Control, 70(10), 2018, 12–23. https://doi.org/10.1016/j.jprocont.2018.07.017.
  2. [2] Z.A. Jaffery, A.K. Dubey, Irshad, and A. Haque, Scheme for predictive fault diagnosis in photo-voltaic modules using thermal imaging, Infrared Physics & Technology, 83(6), 2017, 182–187. https://doi.org/10.1016/j.infrared.2017.04.015
  3. [3] M. Zhang, K.S. Wang, D.D. Wei, and M.J. Zuo, Amplitudes of characteristic frequencies for fault diagnosis of planetary gearbox, Journal of Sound and Vibration, 432, 13(10) 2018, 119–132. https://doi.org/10.1016/j.jsv.2018.06.011
  4. [4] X.G. Wang, R. Jie, and S. Liu, Distribution adaptation and manifold alignment for complex processes fault diagnosis, Knowledge-Based Systems, 156, 15(9), 2018, 100–112. https://doi.org/10.1016/j.knosys.2018.05.023
  5. [5] O. Elimelech and R. Stern, M. Kalech, Structural abstraction for model-based diagnosis with a strong fault model, Knowledge-Based Systems, 161, 14(8), 2018, 357–374. https://doi.org/10.1016/j.knosys.2018.07.039
  6. [6] Q.H. Zhang, Adaptive Kalman filter for actuator fault diagnosis, Automatica, 93(7), 2018, 333–342. https://doi.org/10.1016/j.automatica.2018.03.075
  7. [7] H. Shahnazari and P. Mhaskar, Distributed fault diagnosis for networked nonlinear uncertain systems, Computers & Chemical Engineering, 115, 12(7), 2018, 22–33. https://doi.org/10.1016/j.compchemeng.2018.03.026
  8. [8] H. Liu, J.Z. Zhou, Y. Zheng, W. Jiang, and Y.C. Zhang, Fault diagnosis of rolling bearings with recurrent neural networkbased auto encoders, ISA Transactions, 77(6), 2018, 167–178. https://doi.org/10.1016/j.isatra.2018.04.005
  9. [9] B. Mrugalska, A bounded-error approach to actuator fault diagnosis and remaining useful life prognosis of TakagiSugeno fuzzy systems, ISA Transactions, 80, 2018, 257–266. https://doi.org/10.1016/j.isatra.2018.07.010
  10. [10] A. Ayodeji and Y.K. Liu, Support vector ensemble for incipient fault diagnosis in nuclear plant components, Nuclear Engineering and Technology, 50(8), 2018, 1306–1313. https://doi.org/10.1016/j.net.2018.07.013
  11. [11] I. Martin-Diaz, D. Morinigo-Sotelo, O. Duque-Perez, R. A. Osornio-Rios and R.J. Romero-Troncoso, Hybrid algorithmic approach oriented to incipient rotor fault diagnosis on induction motors, ISA Transactions, 80, 2018, 427–438. https://doi.org/10.1016/j.isatra.2018.07.033
  12. [12] K. Indriawati and N. Sebe, Fault tolerant method on position cascade control of DC servo system, Mechatronic Systems and Control, 48(2), 2020, 144–151. doi: 10.2316/J.2020.201-0094
  13. [13] H.T. Jiang, Q. Chang, Y.L. Wang, and X.J. Xie, Optimization of the active disturbance rejection control of a four-rotor aircraft, Mechatronic Systems and Control, 48(2), 2020, 87–93. doi:10.2316/J.2020.201-0017
  14. [14] J. He, L.C. Shi, C.F. Zhang, J.H. Liu, B.C. Yang, and X.T. Zuo, Optimal adhesion braking control of trains based on parameter estimation and sliding mode observer, Mechatronic Systems and Control, 48(4), 2020, 222–230. doi: 10.2316/J.2020.2010042
  15. [15] S.X. Ding, L.L. Li, and M. Krger, Application of randomized algorithms to assessment and design of observer-based fault detection systems, Automatica, 107, 2019, 175–182. https://doi.org/10.1016/j.automatica.2019.05.037
  16. [16] Z.H. Wang, C.C. Lim, P. Shi, and Y. Shen, H-/L∞ fault detection observer design for linear parameter-varying systems, IFAC-PapersOnLine, 50(1), 2017, 15271–15276. https://doi.org/10.1016/j.ifacol.2017.08.2409
  17. [17] M. Pourasghar, V. Puig, and C. Ocampo-Martinez, Characterisation of interval-observer fault detection and isolation properties using the set-invariance approach, Journal of the Franklin Institute, 357(3), 2019, 1853–1886. https://doi.org/10.1016/j.jfranklin.2019.11.027
  18. [18] J.C.L. Chan, C.P. Tan, H. Trinhb, M.A.S. Kamal, Y.S. Chiew, Robust fault reconstruction for a class of non-infinitely observable descriptor systems using two sliding mode observers in cascade, Applied Mathematics and Computation, 350, 2019, 78–92. https://doi.org/10.1016/j.amc.2018.12.071
  19. [19] C. Liu, G. Vukovich, K.K. Shi, and Z.W. Sun, Robust fault tolerant nonfragile H attitude control for spacecraft via stochastically intermediate observer, Advances in Space Research, 62(9), 2018, 2631–2648. https://doi.org/10.1016/j.asr.2018.07. 026 100
  20. [20] S. Makni, M. Bouattour, and M. Chaabane, Robust observer based Fault Tolerant Tracking Control for T–S uncertain systems subject to sensor and actuator faults, ISA Transactions, 88, 2019, 1–11. https://doi.org/10.1016/j.isatra.2018.11.022
  21. [21] M. Pourasghar, V. Puig, and C. Ocampo-Martinez, Interval observer fault detection ensuring detectability and isolability by using a set-invariance approach, IFAC-PapersOnLine, 51(24), 2018, 1111–1118. https://doi.org/10.1016/j.ifacol.2018.09.727
  22. [22] C.M. Garca, V. Puig, and G.L. Osorio-Gordillo, Robust Fault Estimation based on Interval Takagi-Sugeno Unknown Input Observer, IFAC-PapersOnLine, 51(24), 2018, 508–514. https://doi.org/10.1016/j.ifacol.2018.09.624
  23. [23] J. Banar and S.M. Razavizadeh, Resource allocation and relay selection in full-duplex cooperative orthogonal frequency division multiple access networks, Computers & Electrical Engineering, 61, 2017, 223–234. https://doi.org/10.1016/j.compeleceng.2017.01.019
  24. [24] B. Awoyemi, B. Maharaj, and A. Alfa, Optimal resource allocation solutions for heterogeneous cognitive radio networks, Digital Communications and Networks, 3(2), 2017, 129–139. https://doi.org/10.1016/j.dcan.2016.11.003

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