[1] 上銀科技股份有限公司，滾珠螺桿技術手冊，上銀科技股份有限公司，台灣(2013)
[2] Wang, W. (2011). An inspection model based on a three-stage failure process. Reliability Engineering & System Safety, 96(7), 838-848.
[3] Li, P., Jia, X., Feng, J., Davari, H., Qiao, G., Hwang, Y., & Lee, J. (2018). Prognosability study of ball screw degradation using systematic methodology. Mechanical Systems and Signal Processing, 109, 45-57.
[4] Pandhare, V., Li, X., Miller, M., Jia, X., & Lee, J. (2020). Intelligent diagnostics for ball screw fault through indirect sensing using deep domain adaptation. IEEE Transactions on Instrumentation and Measurement, 70, 1-11.
[5] Chang, J. L., Chao, J. A., Huang, Y. C., & Chen, J. S. (2010). Prognostic experiment for ball screw preload loss of machine tool through the hilbert-huang transform and multiscale entropy method. In The 2010 IEEE International Conference on Information and Automation , 376-380.
[6] Zhang, L., Gao, H., Wen, J., Li, S., & Liu, Q. (2017). A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion. Microelectronics Reliability, 75, 215-222.
[7] Han, C. F., He, H. Q., Wei, C. C., Horng, J. H., Chiu, Y. L., Hwang, Y. C., & Lin, J. F. (2018). Techniques developed for fault diagnosis of long-range running ball screw drive machine to evaluate lubrication condition. Measurement, 126, 274-288.
[8] Shan, P., Lv, H., Yu, L., Ge, H., Li, Y., & Gu, L. (2020). A multisensor data fusion method for ball screw fault diagnosis based on convolutional neural network with selected channels. IEEE Sensors Journal, 20(14), 7896-7905.
[9] Lee, W. G., Lee, J. W., Hong, M. S., Nam, S. H., Jeon, Y., & Lee, M. G. (2015). Failure diagnosis system for a ball-screw by using vibration signals. Shock and Vibration, 2015.
[10] 朱智義, 羅文陽, 顏齊瑩, 李柏霖, 張哲綱, 許竑智, & 林映汝. (2014). 滾珠導螺桿球通頻率暨動力分析. 應用聲學與振動學刊, 6(1), 17-26.
[11] Tsai, P. C., Cheng, C. C., & Hwang, Y. C. (2014). Ball screw preload loss detection using ball pass frequency. Mechanical Systems and Signal Processing, 48(1-2), 77-91.
[12] Wei, C. C., & Lin, J. F. (2003). Kinematic analysis of the ball screw mechanism considering variable contact angles and elastic deformations. J. Mech. Des., 125(4), 717-733.
[13] Yang, Q., Li, X., Wang, Y., Ainapure, A., & Lee, J. (2020). Fault diagnosis of ball screw in industrial robots using non-stationary motor current signals. Procedia Manufacturing, 48, 1102-1108.
[14] Xu, M., Zhang, H., Liu, Z., Li, C., Zhang, Y., Mu, Y., & Hou, C. (2021). A time-dependent dynamic model for ball passage vibration analysis of recirculation ball screw mechanism. Mechanical Systems and Signal Processing, 157, 107632.
[15] Zhou, Y., Mei, X., Zhang, Y., Jiang, G., & Sun, N. (2009). Current-based feed axis condition monitoring and fault diagnosis. In 2009 4th IEEE Conference on Industrial Electronics and Applications (pp. 1191-1195).
[16] Nguyen, T. L., Ro, S. K., & Park, J. K. (2019). Study of ball screw system preload monitoring during operation based on the motor current and screw-nut vibration. Mechanical Systems and Signal Processing, 131, 18-32.
[17] Huang, Y. C., Kao, C. H., & Chen, S. J. (2018). Diagnosis of the hollow ball screw preload classification using machine learning. Applied Sciences, 8(7), 1072.
[18] Peng, Z., Kessissoglou, N. J., & Cox, M. (2005). A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniques. Wear, 258(11-12), 1651-1662.
[19] Horng, J. H., Chern, S. Y., Li, C. L., & Chen, Y. Y. (2017). Surface temperature and wear particle analysis of vertical motion double-nut ball screws. Industrial Lubrication and Tribology.
[20] Peng, Z., & Kessissoglou, N. (2003). An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis. Wear, 255(7-12), 1221-1232.
[21] Li, K., Qiu, C., Li, C., He, S., Li, B., Luo, B., & Liu, H. (2020). Vibration-based health monitoring of ball screw in changing operational conditions. Journal of Manufacturing Processes, 53, 55-68.
[22] Li, F., Jiang, Y., Li, T., & Du, Y. (2017). An improved dynamic model of preloaded ball screw drives considering torque transmission and its application to frequency analysis. Advances in Mechanical Engineering, 9(7), 1687814017710580.
[23] Feng, G. H., & Pan, Y. L. (2012). Investigation of ball screw preload variation based on dynamic modeling of a preload adjustable feed-drive system and spectrum analysis of ball-nuts sensed vibration signals. International Journal of Machine Tools and Manufacture, 52(1), 85-96.
[24] Yadav, S., & Shukla, S. (2016). Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In 2016 IEEE 6th International conference on advanced computing (IACC) (pp. 78-83).
[25] Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai (Vol. 14, No. 2, pp. 1137-1145).
[26] Liashchynskyi, P., & Liashchynskyi, P. (2019). Grid search, random search, genetic algorithm: A big comparison for NAS. arXiv preprint arXiv:1912.06059.
[27] Swana, E., & Doorsamy, W. (2021). An unsupervised learning approach to condition assessment on a wound-rotor induction generator. Energies, 14(3), 602.
[28] Wen, J., Gao, H., Liu, Q., Hong, X., & Sun, Y. (2018). A new method for identifying the ball screw degradation level based on the multiple classifier system. Measurement, 130, 118-127.
[29] Azamfar, M., Li, X., & Lee, J. (2020). Intelligent ball screw fault diagnosis using a deep domain adaptation methodology. Mechanism and Machine Theory, 151, 103932.
[30] Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147, 70-90.
[31] Chen, K., Zu, L., & Wang, L. (2018). Prediction of preload attenuation of ball screw based on support vector machine. Advances in Mechanical Engineering, 10(9), 1687814018799161.
[32] Zheng, B., Yoon, S. W., & Lam, S. S. (2014). Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications, 41(4), 1476-1482.
[33] Santhanam, T., & Padmavathi, M. S. (2015). Application of K-means and genetic algorithms for dimension reduction by integrating SVM for diabetes diagnosis. Procedia Computer Science, 47, 76-83.
[34] El-Thalji, I., & Jantunen, E. (2014). A descriptive model of wear evolution in rolling bearings. Engineering failure analysis, 45, 204-224.
[35] Maru, M. M., Castillo, R. S., & Padovese, L. R. (2007). Study of solid contamination in ball bearings through vibration and wear analyses. Tribology International, 40(3), 433-440.
[36] 張竣閔（2021）。基於擴展派克向量模數與支持向量機之感應馬達故障診斷。國立臺灣科技大學機械工程系碩士論文，台北市。
[37] Kompella, K. D., Mannam, V. G. R., & Rayapudi, S. R. (2016). DWT based bearing fault detection in induction motor using noise cancellation. Journal of Electrical Systems and Information Technology, 3(3), 411-427.
[38] Senthil Kumar, R., & Gerald Christopher Raj, I. (2021). Broken rotor bar fault detection using DWT and energy eigenvalue for DTC fed induction motor drive. International Journal of Electronics, 108(8), 1401-1425.