Performance Complexity Measurement of Tightening Equipment Based on Kolmogorov Entropy
The performance of the tightening equipment will decline with the working process in manufacturing system. The main manifestations are the randomness and discretization degree increasing of the tightening performance. To evaluate the degradation tendency of the tightening performance accurately, a complexity measurement approach based on Kolmogorov entropy is presented. At first, the states of performance index are divided for calibrating the discrete degree. Then the complexity measurement model based on Kolmogorov entropy is built. The model describes the performance degradation tendency of tightening equipment quantitatively. At last, a study case is applied for verifying the efficiency and validity of the approach. The research achievement shows that the presented complexity measurement can effectively evaluate the degradation tendency of the tightening equipment. It can provide theoretical basis for preventive maintenance and life prediction of equipment.
[1] M. Sharma, H. Elmiligi, and F. Gebali. “Performance evaluation of real-time systems,” International Journal of Computing and Digital Systems, vol. 3, no. 3, pp. 43-52, Jan. 2015.
[2] U. Fahrenberg, K. G. Larsen, and A. Legay. Model-based verification, optimization, synthesis and performance evaluation of real-time systems. New York: Springer-Verlag, 2013, pp. 67-108.
[3] R. Y. Jung, and P. B. Shing. “Performance evaluation of a real-time pseudodynamic test system,” Earthquake Engineering & Structural Dynamics, vol. 35, no. 35, pp. 789-810, 2006.
[4] Y. Pan, and J. Chen. “The changes of complexity in the performance degradation process of rolling element bearing,” Journal of Vibration & Control, vol. 22, no. 2, pp. 1-14, July. 2014.
[5] T. Sadok, and R. Nidhal, “Performance evaluation of a closed loop manufacturing system taking into account delivery activity and degradation,” Industrial Engineering and Systems Management (IESM), 2015 International Conference on. IEEE, Spain, 2015, pp. 469-474.
[6] T. Sadok, H. Zied, and R. Nidhal, “Performance evaluation of a hybrid manufacturing remanufacturing system taking into account the machine degradation,” Ifac, vol. 48, no. 3, pp. 2153-2157, 2015.
[7] H. Li, Z. Yang, B. Xu. and G. Liu, “Reliability evaluation of NC machine tools considering working conditions,” Mathematical Problems in Engineering, vol. 2016, no. 21,pp. 1-11, 2016.
[8] B K Lad, and M S Kulkarn, “Reliability and maintenance based design of machine tools,” International Journal of Performability Engineering, vol. 9, no. 3,pp. 321-332, 2013.
[9] C. Lv, A. Li, and L. Xu, “The research of performance evaluation system on manufacturing system with reconfigurable configuration,” Mechatronics and Automation (ICMA), 2007 International Conference on. IEEE, 2007, pp. 1005-1010.
[10] X. C. Zhang, H. L. Gao, H. F. Huang, L. Guo, and Y. W. Zhang, “Performance degradation assessment technology for screw of NC machine tool,” Jisuanji Jicheng Zhizao Xitong/computer Integrated Manufacturing Systems Cims, vol. 21, no. 5, pp. 1293-1300, 2015.
[11] F. Zhou, A. Li, N. Xie, L. Y. Xu, and H. Shao, “Reliability analysis of multi-state manufacturing systems based on performance degradation,” Jisuanji Jicheng Zhizao Xitong/computer Integrated Manufacturing Systems Cims, vol. 20, no. 6, pp. 1424-1431, 2014.
[12] K. Efthymiou, D. Mourtzis, A. Pagoropoulos, N. Papakostas, and G. Chryssolouris, “Manufacturing systems complexity analysis methods review,” International Journal of Computer Integrated Manufacturing, vol. 29, no. 9, pp. 1025-1044, 2016.
[13] R. Yan, and R. X. Gao, “Complexity as a measure for machine health evaluation,” IEEE Transactions on Instrumentation & Measurement, vol. 53, no. 4, pp. 1327-1334, 2004.
[14] B. Yu, Y. H. Li, and P. Zhang, “Application of correlation dimension and Kolmogorov entropy in aeroengine fault diagnosis.” Journal of Aerospace Power, vol. 21, no. 1, pp. 219-224, 2006.
[15] R. Yan, and R. X. Gao, “Complexity measure: A nonlinear time series analysis technique for machine health monitoring,” Structural Health Monitoring 2007: Quantification, Validation, and Implementation - Proceedings of the 6th International Workshop on Structural Health Monitoring (IWSHM), 2007, vol. 2, pp. 1291-1298,
[16] X. Dong, and W. Zhang, “Degradation analysis of grinding machine spindle systems based on complexity,” Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, vol. 229, no. 8, pp. 1467-1471, 2015.
[17] K Efthymiou, A Pagoropoulos, N Papakostas, D Mourtzis, and G Chryssolouris, “Manufacturing systems complexity: An assessment of manufacturing performance indicators unpredictability”. CIRP Journal of Manufacturing Science & Technology, vol. 7, no. 4, pp. 324-334, 2014.
[1] M. Sharma, H. Elmiligi, and F. Gebali. “Performance evaluation of real-time systems,” International Journal of Computing and Digital Systems, vol. 3, no. 3, pp. 43-52, Jan. 2015.
[2] U. Fahrenberg, K. G. Larsen, and A. Legay. Model-based verification, optimization, synthesis and performance evaluation of real-time systems. New York: Springer-Verlag, 2013, pp. 67-108.
[3] R. Y. Jung, and P. B. Shing. “Performance evaluation of a real-time pseudodynamic test system,” Earthquake Engineering & Structural Dynamics, vol. 35, no. 35, pp. 789-810, 2006.
[4] Y. Pan, and J. Chen. “The changes of complexity in the performance degradation process of rolling element bearing,” Journal of Vibration & Control, vol. 22, no. 2, pp. 1-14, July. 2014.
[5] T. Sadok, and R. Nidhal, “Performance evaluation of a closed loop manufacturing system taking into account delivery activity and degradation,” Industrial Engineering and Systems Management (IESM), 2015 International Conference on. IEEE, Spain, 2015, pp. 469-474.
[6] T. Sadok, H. Zied, and R. Nidhal, “Performance evaluation of a hybrid manufacturing remanufacturing system taking into account the machine degradation,” Ifac, vol. 48, no. 3, pp. 2153-2157, 2015.
[7] H. Li, Z. Yang, B. Xu. and G. Liu, “Reliability evaluation of NC machine tools considering working conditions,” Mathematical Problems in Engineering, vol. 2016, no. 21,pp. 1-11, 2016.
[8] B K Lad, and M S Kulkarn, “Reliability and maintenance based design of machine tools,” International Journal of Performability Engineering, vol. 9, no. 3,pp. 321-332, 2013.
[9] C. Lv, A. Li, and L. Xu, “The research of performance evaluation system on manufacturing system with reconfigurable configuration,” Mechatronics and Automation (ICMA), 2007 International Conference on. IEEE, 2007, pp. 1005-1010.
[10] X. C. Zhang, H. L. Gao, H. F. Huang, L. Guo, and Y. W. Zhang, “Performance degradation assessment technology for screw of NC machine tool,” Jisuanji Jicheng Zhizao Xitong/computer Integrated Manufacturing Systems Cims, vol. 21, no. 5, pp. 1293-1300, 2015.
[11] F. Zhou, A. Li, N. Xie, L. Y. Xu, and H. Shao, “Reliability analysis of multi-state manufacturing systems based on performance degradation,” Jisuanji Jicheng Zhizao Xitong/computer Integrated Manufacturing Systems Cims, vol. 20, no. 6, pp. 1424-1431, 2014.
[12] K. Efthymiou, D. Mourtzis, A. Pagoropoulos, N. Papakostas, and G. Chryssolouris, “Manufacturing systems complexity analysis methods review,” International Journal of Computer Integrated Manufacturing, vol. 29, no. 9, pp. 1025-1044, 2016.
[13] R. Yan, and R. X. Gao, “Complexity as a measure for machine health evaluation,” IEEE Transactions on Instrumentation & Measurement, vol. 53, no. 4, pp. 1327-1334, 2004.
[14] B. Yu, Y. H. Li, and P. Zhang, “Application of correlation dimension and Kolmogorov entropy in aeroengine fault diagnosis.” Journal of Aerospace Power, vol. 21, no. 1, pp. 219-224, 2006.
[15] R. Yan, and R. X. Gao, “Complexity measure: A nonlinear time series analysis technique for machine health monitoring,” Structural Health Monitoring 2007: Quantification, Validation, and Implementation - Proceedings of the 6th International Workshop on Structural Health Monitoring (IWSHM), 2007, vol. 2, pp. 1291-1298,
[16] X. Dong, and W. Zhang, “Degradation analysis of grinding machine spindle systems based on complexity,” Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, vol. 229, no. 8, pp. 1467-1471, 2015.
[17] K Efthymiou, A Pagoropoulos, N Papakostas, D Mourtzis, and G Chryssolouris, “Manufacturing systems complexity: An assessment of manufacturing performance indicators unpredictability”. CIRP Journal of Manufacturing Science & Technology, vol. 7, no. 4, pp. 324-334, 2014.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:75214", author = "Guoliang Fan and Aiping Li and Xuemei Liu and Liyun Xu", title = "Performance Complexity Measurement of Tightening Equipment Based on Kolmogorov Entropy", abstract = "The performance of the tightening equipment will decline with the working process in manufacturing system. The main manifestations are the randomness and discretization degree increasing of the tightening performance. To evaluate the degradation tendency of the tightening performance accurately, a complexity measurement approach based on Kolmogorov entropy is presented. At first, the states of performance index are divided for calibrating the discrete degree. Then the complexity measurement model based on Kolmogorov entropy is built. The model describes the performance degradation tendency of tightening equipment quantitatively. At last, a study case is applied for verifying the efficiency and validity of the approach. The research achievement shows that the presented complexity measurement can effectively evaluate the degradation tendency of the tightening equipment. It can provide theoretical basis for preventive maintenance and life prediction of equipment.
", keywords = "Complexity measurement, Kolmogorov entropy, manufacturing system, performance evaluation, tightening equipment.", volume = "11", number = "3", pages = "681-6", }
