x(t) and y(t)
References
1 1Chen, B., Chen, X., Li, B. et al. (2011). Reliability estimation for cutting tools based on logistic regression model using vibration signals. Mechanical System and Signal Process 25 (7): 2526–2537. https://doi.org/10.1016/j.ymssp.2011.03.001.
2 2Suprock, C.A. and Nichols, J.S. (2009). A low cost wireless high bandwidth transmitter for sensor‐integrated metal cutting tools and process monitoring. International Journal of Mechatronics and Manufacturing Systems 2 (4): 441–454. https://doi.org/10.1504/IJMMS.2009.027128.
3 3Ghosh, 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. https://doi.org/10.1016/j.ymssp.2005.10.010.
4 4Abuthakeer, S.S., Mohanram, P.V., and Kumar, G.M. (2011). Prediction and control of cutting tool vibration CNC lathe with ANOVA and ANN. International Journal of Lean Thinking 2 (1): 1–23.
5 5Tieng, H., Li, Y.Y., Tseng, K.P. et al. (2020). An Automated Dynamic‐Balancing‐Inspection Scheme for Wheel Machining. IEEE Robotics and Automation Letters 5 (4): 2224–2231. https://doi.org/10.1109/LRA.2020.2970953.
6 6Abellan‐Nebot, J.V. and Subirón, F.R. (2010). A review of machining monitoring systems based on artificial intelligence process models. The International Journal of Advanced Manufacturing Technology 47 (14): 237–257. https://doi.org/10.1007/s00170‐009‐2191‐8.
7 7Teti, R., Jemielniak, K., O’Donnell, G. et al. (2010). Advanced monitoring of machining operations. CIRP Annals 59 (2): 717–739. https://doi.org/10.1016/j.cirp.2010.05.010.
8 8Yang, H.C., Tieng, H., and Cheng, F.T. (2015). Total precision inspection of machine tools with virtual metrology. Journal of the Chinese Institute of Engineers 39 (2): 221–235. https://doi.org/10.1109/CoASE.2015.7294301.
9 9Mallat, S.G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (7): 674–693. https://doi.org/10.1109/34.192463.
10 10Zhang, Z., Wang, Y., and Wang, K. (2013). Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. Journal of Intelligent Manufacturing 24: 1213–1227. https://doi.org/10.1007/s10845‐012‐0657‐2.
11 11Kim, J., Lee, H., Jeon, J.W. et al. (2020). Stacked auto‐encoder based CNC tool diagnosis using discrete wavelet transform feature extraction. Processes 8 (4): 456. https://doi.org/10.3390/pr8040456.
12 12Jiang, G., He, H., Xie, P. et al. (2017). Stacked multilevel‐denoising autoencoders: a new representation learning approach for wind turbine gearbox fault diagnosis. IEEE Transactions on Instrumentation and Measurement 66 (9): 2391–2402. https://doi.org/10.1109/TIM.2017.2698738.
13 13Lee, R.J. and Nicewander, W.A. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician 42 (1): 59–66. https://doi.org/10.1080/00031305.1988.10475524.
14 14Strang,