Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine

Article English OPEN
Tran, Van Tung ; Pham, Hong Thom ; Yang, Bo-Suk ; Nguyen, Tan Tien (2012)

Machine performance degradation assessment and remaining useful life (RUL) prediction are of crucial importance in condition-based maintenance to reduce the maintenance cost and improve the reliability. They provide a potent tool for operators in decision-making by specifying the present machine state and estimating the remaining time. For this ultimate purpose, a three-stage method for assessing the machine health degradation and forecasting the RUL is proposed. In the first stage, only the normal operating condition of machine is used to create identification model for recognizing the dynamic system behavior. Degradation index which is used for indicating the machine degradation is subsequently created based on the root mean square of residual errors. These errors are the difference between identification model and behavior of system. In the second stage, the Cox’s proportional hazard model is generated to estimate the survival function of the system. In the last stage, support vector machine, which is one of the remarkable machine learning techniques, in association with time-series techniques is utilized to forecast the RUL. The data of low methane compressor acquired from condition monitoring routine is used for validating the proposed method. The result shows that the proposed method could be used as a reliable tool to machine prognostics.
  • References (15)
    15 references, page 1 of 2

    [1] A. Heng, S. Zhang, A. C. C. Tan, J. Mathew, Rotating machinery prognostics: state of the art, challenges and opportunities, Mechanical Systems and Signal Processing 23 (2009) 724-739.

    [2] H. Qiu, J. Lee, J. Lin, G. Yu, Robust performance degradation assessment methods for enhanced rolling element bearing prognostics, Advanced Engineering Informatics 17 (2003) 127-140.

    [3] J. Lee, Measurement of machine performance degradation using a neural network model, Computer in Industry 30 (1996) 193-209

    [4] J. Lee, B. M. Kramer, Monitor machine degradation using an enhanced CMAC neural network, IEEE International Conference on Systems, Man and Cybernetics 2 (1992) 1010- 1015.

    [5] C. C. Lin, H. Y. Tseng, A neural network application for reliability modeling and condition-based predictive maintenance, International Journal of Advanced Manufacturing Technology 25 (2005) 174-179

    [6] R. Z. Xu, L. Xie, M. C. Zhang, Machine degradation analysis using fuzzy CMAC neural network approach, International Journal of Advanced Manufacturing Technology 36 (2008) 765-772.

    [7] N. Gebraeel, M. Lawley, R. Liu, V. Parmeshwaran, Residual life prediction from vibration-based degradation signals: a neural network approach, IEEE Trans. Industrial Electronics 51 (2004) 694- 700.

    [8] R. Huang, L. Xi, X. Li, C. R. Liu, H. Qiu, J. Lee, Residual life predictions for ball bearing based on self-organizing map and back propagation neural networks methods, Mechanical Systems and Signal Processing 21 (2007) 193-207.

    [9] L. Liao, J. Lee, A novel method for machine performance degradation assessment based on fixed cycle feature test, Journal of Sound and Vibration 326 (2009) 894-908.

    [10] Y. Xu, C. Deng, J. Wu, Least squares support vector machines for performance degradation modeling of CNC equipments, International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2009, pp. 201-206

  • Similar Research Results (2)
  • Metrics
    0
    views in OpenAIRE
    0
    views in local repository
    408
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    University of Huddersfield Repository - IRUS-UK 0 408
Share - Bookmark