Lei Wang, Chaomin Luo, Jingcao Cai, and Ming Li


  1. [1] B. Martin, O. Jun, O. Burak, E.D. Richard, S.G. Kapoor,K. Andreas, and S. Georges, Evaluation of a spindle-based forcesensor for monitoring and fault diagnosis of machining opera-tions, International Journal of Machine Tools & Manufacture,42(6), 2002, 741–751.
  2. [2] Y.X. Wang, J.W. Xiang, R. Markert, and M. Liang, Spectralkurtosis for fault detection, diagnosis and prognostics of rotat-ing machines: A review with applications, Mechanical Systemsand Signal Processing, 66–67(1), 2016, 679–698.
  3. [3] B. Nawel, D. Saber, H.G. Sonia, and P. Henri, Fault detec-tion, diagnosis and recovery using artificial immune systems:A review, Engineering Applications of Artificial Intelligence,46(11), 2015, 43–57.
  4. [4] H.Y. Yu, F. Khan, and V. Garaniya, A probabilistic multivari-ate method for fault diagnosis of industrial processes, ChemicalEngineering Research and Design, 104(12), 2015, 306–318.
  5. [5] J. Lee, M. Ghaffari, and S. Elmeligy, Self-maintenance andengineering immune systems: Towards smarter machines andmanufacturing systems, Annual Reviews in Control, 35(1),2011, 111–122.
  6. [6] O. Janssens, R. Schulz, V. Slavkovikj, K. Stockman,M. Loccufier, R.V. Walle, and S.V. Hoecke, Thermal imagebased fault diagnosis for rotating machinery, Infrared Physics& Technology, 73(6), 2015, 78–87.
  7. [7] P.J. Cheng and H.P. Huang, Robust fault detection and isola-tion using bond graph for an active–passive variable serial elas-tic actuator, International Journal of Robotics and Automation(IJRA), 6(2), 2015, 29–47.
  8. [8] C.K. Lau, Y.S. Heng, M.A. Hussain, and M.I. Mohamad Nor,Fault diagnosis of the polypropylene production process usingANFIS, ISA Transactions, 49(4), 2010, 559–566.
  9. [9] A. Cheng and K. Lipheng, A model-based approach to faultdiagnosis of FMS, Proc. IEEE Symposium on Emerging Tech-nologies and Factory Automation (ETFA), Kauai, HI, 1996,254–260.
  10. [10] L. Mohamed and A.S. Ibrahim, Model-based fault diagnosis viaparameter estimation using knowledge based and fuzzy logicapproach, Proc. 11th IEEE Mediterranean ElectrotechnicalConf., Cairo, Egypt, 2002, 505–509.
  11. [11] H. John, Model-based fault detection in information poorplants, Automatica, 30(6), 1994, 929–943.
  12. [12] A. Prakash, N. Khilwani, M.K. Tiwari, and Y. Cohen, Modi-fied immune algorithm for job selection and operation alloca-tion problem in flexible manufacturing systems, Advances inEngineering Software, 39(3), 2008, 219–232.
  13. [13] X.H. Li, Z.X. Wang, T.Y. Lu, and X.J. Che, Modeling immunesystem: Principles, models, analysis and perspectives, Journalof Bionic Engineering, 6(1), 2009, 77–85.
  14. [14] R. Challoo, P. Rao, S. Ozcelik, L. Challoo, and S. Li, Nav-igation control and path mapping of a mobile robot usingartificial immune systems, International Journal of Roboticsand Automation(IJRA), 1(1), 2010, 1–25.
  15. [15] S.F. Li, X.L. Wang, J.Z. Xiao, and Z.J. Yin, Self-adaptiveobtaining water-supply reservoir operation rules: Co-evolutionartificial immune system, Expert Systems with Applications,41(4), 2014, 1262–1270.
  16. [16] B. Chen, Agent-based artificial immune system approach foradaptive damage detection in monitoring networks, Journal ofNetwork and Computer Applications, 33(6), 2010, 633–645.
  17. [17] J. Barbosa, P. Leitao, E. Adam, and D. Trentesaux, Dynamicself-organization in holonic multi-agent manufacturing systems:The ADACOR evolution, Computers in Industry, 66(1), 2015,99–111.
  18. [18] P. Leitao, J. Barbosa, and D. Trentesaux, Bio-inspired multi-agent systems for reconfigurable manufacturing systems, En-gineering Applications of Artificial Intelligence, 25(5), 2012,934–944.
  19. [19] J. Barbosa, P. Leitao, and A.I. Pereira, Combining adaptationand optimization in bioinspired multi-agent manufacturing sys-tems, Proc. 2011 IEEE International Symposium on IndustrialElectronics, Gdansk, Poland, 2011, 1773–1778.
  20. [20] Y. Cheung and J.H. Chung, Semi-autonomous control of amulti-agent robotic system for multi-target operations, Inter-national Journal of Robotics and Automation (IJRA), 2(2),2011, 107–127.
  21. [21] E. Rizvan, C. Sahin, A. Baykasoglu, and V. Kaplanoglu,A multi-agent based approach to dynamic scheduling of ma-chines and automated guided vehicles in manufacturing sys-tems, Applied Soft Computing, 12(6), 2012, 1720–1732.
  22. [22] W.W. Koczkodaj, A new definition of consistency of pairwisecomparisons, Mathematical and Computer Modelling, 18(7),1993, 79–84.
  23. [23] T.L. Satty, A scaling method for priorities in hierarchicalstructures, Journal of Mathematical Psychology, 15(7), 1977,234–281.
  24. [24] T.L Saaty, The analytic hierarchy process (New York: McGraw-Hill Press, 1980).
  25. [25] T.L Saaty and L.G. Vargas, Decision making in economic, po-litical, social, and technological environments with the analytichierarchy process (Pittsburgh, PA: RWS Press, 1994).
  26. [26] L. Wang, D.B. Tang, W.B. Gu, K. Zheng, W.D. Yuan, andD.S. Tang, Pheromone-based coordination for manufacturingsystem control, Journal of Intelligent Manufacturing, 23(3),2012, 747–757.85

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