Journal of Intelligent Manufacturing

, Volume 30, Issue 1, pp 113–122 | Cite as

A new tool wear monitoring method based on multi-scale PCA

  • Guofeng WangEmail author
  • Yanchao Zhang
  • Chang Liu
  • Qinglu Xie
  • Yonggang Xu


A multi-scale principal component analysis (MSPCA) method is presented to realize online tool wear monitoring of milling process. In this method, the training sample set of normal operational condition is decomposed into different scales using wavelet multi resolution analysis. The statistical indices and the corresponding control limits are constructed to monitor the tool wear based on principal component analysis (PCA). By integration of PCA with wavelet transformation, the accuracy and robustness of tool wear monitoring model can be improved greatly. To test the effectiveness of the proposed method, a Ti–6Al–4V milling experiment was carried out. Force and vibration signals during the machining process were collected simultaneously to depict the characteristics of the tool wear variation. Based on the extracted root mean square and kurtosis features, the tool wear monitoring is realized by MSPCA and PCA respectively. The analysis and comparison results show that MSPCA can produce higher accuracy in comparison with PCA.


Tool wear monitoring Multi-scale principal component analysis Milling process Wavelet transformation 



This project is supported by National Natural Science Foundation of China (51175371 and 51420105007), National Science and Technology Major Projects (2014ZX04012-014), and Tianjin Science and Technology Support Program (13ZCZDGX04000) and (14ZCZDGX00021).


  1. Bakshi, B. R. (1998). Multiscale PCA with application to multivariate statistical process monitoring. AIChE Journal, 44(7), 1596–1610.CrossRefGoogle Scholar
  2. Brezak, D., Majetic, D., Udiljak, T., et al. (2012). Tool wear estimation using an analytic fuzzy classifier and support vector machines. Journal of Intelligent Manufacturing, 23(3), 797–809.CrossRefGoogle Scholar
  3. Chen, J., Bandoni, A., & Romagnoli, J. A. (1996). Robust statistical process monitoring. Computers & Chemical Engineering, 20, S497–S502.CrossRefGoogle Scholar
  4. Chen, B., Chen, X., Li, B., et al. (2011). Reliability estimation for cutting tools based on logistic regression model using vibration signals. Mechanical Systems and Signal Processing, 25(7), 2526–2537.CrossRefGoogle Scholar
  5. Cho, S., Binsaeid, S., & Asfour, S. (2010). Design of multisensor fusion-based tool condition monitoring system in end milling. The International Journal of Advanced Manufacturing Technology, 46(5–8), 681–694.CrossRefGoogle Scholar
  6. Fu, P., Li, W., & Guo, L. (2011). Fuzzy clustering and visualization analysis of tool wear status recognition. Procedia Engineering, 23, 479–486.CrossRefGoogle Scholar
  7. Ghosh, N., Ravi, Y. B., Patra, A., et al. (2007). Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mechanical Systems and Signal Processing, 21(1), 466–479.CrossRefGoogle Scholar
  8. Grasso, M., Albertelli, P., & Colosimo, B. M. (2013). An adaptive SPC approach for multi-sensor fusion and monitoring of time-varying processes. Procedia CIRP, 12, 61–66.CrossRefGoogle Scholar
  9. Humberstone, M., Wood, B., Henkel, J., et al. (2012). Differentiating between expanded and fault conditions using principal component analysis. Journal of Intelligent Manufacturing, 23(2), 179–188.CrossRefGoogle Scholar
  10. JiJi, R. D., Hammond, M. H., Williams, F. W., et al. (2003). Multivariate statistical process control for continuous monitoring of networked early warning fire detection (EWFD) systems. Sensors and Actuators B: Chemical, 93(1), 107–116.CrossRefGoogle Scholar
  11. Lachouri, A., Baiche, K., Djeghader, R., et al. (2008). Analyze and fault diagnosis by multi-scale PCA. In ICTTA 2008. 3rd International conference on information and communication technologies: From theory to applications, 2008. IEEE (pp. 1–6).Google Scholar
  12. Lee, D. S., Park, J. M., & Vanrolleghem, P. A. (2005). Adaptive multiscale principal component analysis for on-line monitoring of a sequencing batch reactor. Journal of Biotechnology, 116(2), 195–210.CrossRefGoogle Scholar
  13. MacGregor, J. F., & Kourti, T. (1995). Statistical process control of multivariate processes. Control Engineering Practice, 3(3), 403–414.CrossRefGoogle Scholar
  14. Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693.CrossRefGoogle Scholar
  15. Misra, M., Yue, H. H., Qin, S. J., et al. (2002). Multivariate process monitoring and fault diagnosis by multi-scale PCA. Computers & Chemical Engineering, 26(9), 1281–1293.Google Scholar
  16. Moradi, H., Vossoughi, G., Movahhedy, M. R., et al. (2013). Forced vibration analysis of the milling process with structural nonlinearity, internal resonance, tool wear and process damping effects. International Journal of Non-linear Mechanics, 54, 22–34.Google Scholar
  17. Mujica, L. E., Vehi, J., Ruiz, M., et al. (2008). Multivariate statistics process control for dimensionality reduction in structural assessment. Mechanical Systems and Signal Processing, 22(1), 155–171.CrossRefGoogle Scholar
  18. Padilla, M., Perera, A., Montoliu, I., et al. (2010). Fault detection, identification, and reconstruction of faulty chemical gas sensors under drift conditions using Principal Component Analysis and Multiscale-PCA. In The 2010 international joint conference on neural networks (IJCNN). IEEE (pp. 1–7).Google Scholar
  19. Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2, 559–572.Google Scholar
  20. Salgado, D. R., & Alonso, F. J. (2007). An approach based on current and sound signals for in-process tool wear monitoring. International Journal of Machine Tools and Manufacture, 47(14), 2140–2152.CrossRefGoogle Scholar
  21. Segreto, T., Simeone, A., & Teti, R. (2013). Multiple sensor monitoring in nickel alloy turning for tool wear assessment via sensor fusion. Procedia CIRP, 12, 85–90.CrossRefGoogle Scholar
  22. Sharma, L. N., Dandapat, S., & Mahanta, A. (2012). Multichannel ECG data compression based on multiscale principal component analysis. IEEE Transactions on Information Technology in Biomedicine, 16(4), 730–736.CrossRefGoogle Scholar
  23. Simoglou, A., Martin, E. B., & Morris, A. J. (2002). Statistical performance monitoring of dynamic multivariate processes using state space modeling. Computers & Chemical Engineering, 26(6), 909–920.CrossRefGoogle Scholar
  24. Tobon-Mejia, D. A., Medjaher, K., & Zerhouni, N. (2012). CNC machine tool’s wear diagnostic and prognostic by using dynamic Bayesian networks. Mechanical Systems and Signal Processing, 28, 167–182.CrossRefGoogle Scholar
  25. Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., et al. (2003). A review of process fault detection and diagnosis: Part III: Process history based methods. Computers & Chemical Engineering, 27(3), 327–346.CrossRefGoogle Scholar
  26. Wang, G., & Cui, Y. (2013). On line tool wear monitoring based on auto associative neural network. Journal of Intelligent Manufacturing, 24(6), 1085–1094.CrossRefGoogle Scholar
  27. Wang, G., & Feng, X. (2013). Tool wear state recognition based on linear chain conditional random field model. Engineering Applications of Artificial Intelligence, 26(4), 1421–1427.CrossRefGoogle Scholar
  28. Xiao, Y., Wang, H., & Xu, W. (2014). Model selection of Gaussian kernel PCA for novelty detection. Chemometrics and Intelligent Laboratory Systems, 136, 164–172.CrossRefGoogle Scholar
  29. Yin, S., & Huang, Z. (2014). Performance monitoring for vehicle suspension system via fuzzy positivistic C-means clustering based on accelerometer measurements. IEEE/ASME Transactions on Mechatronics, 20(5), 2613–2620.CrossRefGoogle Scholar
  30. Yoon, S., & MacGregor, J. F. (2004). Principal-component analysis of multiscale data for process monitoring and fault diagnosis. AIChE Journal, 50(11), 2891–2903.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Guofeng Wang
    • 1
    Email author
  • Yanchao Zhang
    • 1
  • Chang Liu
    • 1
  • Qinglu Xie
    • 1
  • Yonggang Xu
    • 1
  1. 1.Key Laboratory of Mechanism Theory and Equipment Design of Ministry of EducationTianjin UniversityTianjinChina

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