1. Wang, G., He, Z., Chen, X., et al.: “Where to go” for basic research on mechanical fault diagnosis. J. Mech. Eng. 01, 63–72 (2013)
2. Lamraoui, M., Barakat, M., Thomas, M., Badaoui, M.E.: Chatter detection in milling machines by neural network classification and feature selection. J. Vib. Control 21(7), 1251–1266 (2015)
3. Li, K., He, S., Li, B., Liu, H., Mao, X., Shi, C.: A novel online chatter detection method in milling process based on multiscale entropy and gradient tree boosting. Mech. Syst. Signal Process. 135, 106385 (2020)
4. Liu, C., Xu, W., Gao, L.: Identification of milling chatter based on a novel frequency-domain search algorithm. Int. J. Adv. Manuf. Technol. 109, 2393–2407 (2013)
5. Dan, Z., Song, Z., Li, Z., et al.: Sensor fault diagnosis based on wavelet analysis and LSTM neural network. In: 2022 IEEE 20th International Power Electronics and Motion Control Conference (PEMC), pp. 249–255. IEEE (2020)