本研究目的為搭配智能化對稱雙主軸研磨機開發一套監控系統，雙主軸研磨機是用來製造碳化鎢組成的LED探針，此監控系統包含三個部份: (1)藉由架設在高速主軸上的類比加速規擷取振動訊號。 (2)資料擷取卡模組會取樣類比訊號並將訊號轉換為數位訊號儲存。 (3)透過Visual C#語言可以將擷取出的數位訊號利用演算法進行分析。
透過監控系統可以達到以下幾點:
1.監控系統可以偵測出聚晶鑽石磨輪上的鈍化、填塞、磨損現象。
2.根據振動訊號調整LED探針的製程參數，使雙主軸研磨機有更好的加工性能。
3.透過軸承的異常偵測演算法能夠提早偵測到主軸的異常發生，監控軸承會發現到主軸隨著運轉的時間增加造成加工性能衰退，這在預知保養是非常重要的一環。至今，旋轉機械的複雜程度已經日異月新，當機械發生異常時，操作者不可能即時透過肉眼找出機械的問題所在，因此發展可以即早偵測出異常且避免突然發生的故障的預知保養系統是一件重要的工作，本研究提出方均根、峭度、排序熵及支持向量資料描述法當作異常偵測的方法。
根據實驗結果證明本論文所開發監控系統的效率性及可行性。

The objective of this study is to develop a monitoring system for intellectualized symmetric high‐speed dual‐spindles grinding used for WC LED‐probe fabrications. This monitoring system consists of three parts: 1) vibration signals collected by analog accelerometers installed on the spindle; 2) the analog signals are sampled and converted into digital numeric values via data acquisition (DAQ) modules; 3) the resulting digital samples are then analyzed by using algorithms implemented by Visual C# language.
Based on this monitoring system, the following tasks were performed:
1. The wear, clog and dulling in the PCD wheel can be detected out from the developed monitoring system.
2. The process parameters for LED‐probe fabrications are adjusted to obtain a better performance according to the vibration signals.
3. The anomaly of the spindle can be detected early by a novelty bearing anomaly detection algorithm. Monitoring the performance degradation of bearings in spindle continuously is necessary for predictive maintenance. Yet, the complexity of rotary machine increases dramatically and it is impossible for an operator to keeping an eye on the machine’s condition daily. Therefore, it is essential to develop a prognostics system that has the ability to detect abnormality early and prevent unexpected failure. In this study, a data-driven prognostic methodology based on RMS, Kurtosis, Permutation Entropy and support vector data description (SVDD) is proposed.
The experimental results demonstrates the efficiency and feasibility of the developed monitoring system.

[1] J. Zarei and J. Poshtan, "Bearing fault detection using wavelet packet transform of induction motor stator current," Tribology International, vol. 40, pp. 763-769, May 2007.
[2] P. Scanlon, D. F. Kavanagh, and F. M. Boland, "Residual Life Prediction of Rotating Machines Using Acoustic Noise Signals," IEEE Transactions on Instrumentation and Measurement, vol. 62, pp. 95-108, Jan 2013.
[3] X. R. Zhu, Y. Y. Zhang, and Y. S. Zhu, "Bearing performance degradation assessment based on the rough support vector data description," Mechanical Systems and Signal Processing, vol. 34, pp. 203-217, Jan 2013.
[4] Y. Zhang, H. F. Zuo, and F. Bai, "Classification of fault location and performance degradation of a roller bearing," Measurement, vol. 46, pp. 1178-1189, Apr 2013.
[5] M. D. Prieto, G. Cirrincione, A. G. Espinosa, J. A. Ortega, and H. Henao, "Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks," IEEE Transactions on Industrial Electronics, vol. 60, pp. 3398-3407, Aug 2013.
[6] L. Mi, W. Tan, and R. Chen, "Multi-steps degradation process prediction for bearing based on improved back propagation neural network," Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, vol. 227, pp. 1544-1553, Jul 2013.
[7] S. J. Dong and T. H. Luo, "Bearing degradation process prediction based on the PCA and optimized LS-SVM model," Measurement, vol. 46, pp. 3143-3152, Nov 2013.
[8] J. B. Yu, "Local and Nonlocal Preserving Projection for Bearing Defect Classification and Performance Assessment," IEEE Transactions on Industrial Electronics, vol. 59, pp. 2363-2376, May 2012.
[9] B. Samanta, K. R. Al-Balushi, and S. A. Al-Araimi, "Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection," Engineering Applications of Artificial Intelligence, vol. 16, pp. 657-665, Oct-Dec 2003.
[10] B. Li, M. Y. Chow, Y. Tipsuwan, and J. C. Hung, "Neural-network-based motor rolling bearing fault diagnosis," IEEE Transactions on Industrial Electronics, vol. 47, pp. 1060-1069, Oct 2000.
[11] T. I. Liu and J. M. Mengel, "Intelligent Monitoring of Ball-Bearing Conditions," Mechanical Systems and Signal Processing, vol. 6, pp. 419-431, Sep 1992.
[12] N. G. Nikolaou and I. A. Antoniadis, "Rolling element bearing fault diagnosis using wavelet packets," Ndt & E International, vol. 35, pp. 197-205, Apr 2002.
[13] S. Abbasion, A. Rafsanjani, A. Farshidianfar, and N. Irani, "Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine," Mechanical Systems and Signal Processing, vol. 21, pp. 2933-2945, Oct 2007.
[14] X. S. Lou and K. A. Loparo, "Bearing fault diagnosis based on wavelet transform and fuzzy inference," Mechanical Systems and Signal Processing, vol. 18, pp. 1077-1095, Sep 2004.
[15] Y. J. Wang, S. Q. Kang, Y. C. Jiang, G. X. Yang, L. X. Song, and V. I. Mikulovich, "Classification of fault location and the degree of performance degradation of a rolling bearing based on an improved hyper-sphere-structured multi-class support vector machine," Mechanical Systems and Signal Processing, vol. 29, pp. 404-414, May 2012.
[16] S. D. Wu, C. W. Wu, T. Y. Wu, and C. C. Wang, "Multi-Scale Analysis Based Ball Bearing Defect Diagnostics Using Mahalanobis Distance and Support Vector Machine," Entropy, vol. 15, pp. 416-433, Feb 2013.
[17] L. Zhang, G. L. Xiong, H. S. Liu, H. J. Zou, and W. Z. Guo, "Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference," Expert Systems with Applications, vol. 37, pp. 6077-6085, Aug 2010.
[18] S. D. Wu, C. W. Wu, S. G. Lin, C. C. Wang, and K. Y. Lee, "Time Series Analysis Using Composite Multiscale Entropy," Entropy, vol. 15, pp. 1069-1084, Mar 2013.
[19] S. D. Wu, C. W. Wu, K. Y. Lee, and S. G. Lin, "Modified multiscale entropy for short-term time series analysis," Physica a-Statistical Mechanics and Its Applications, vol. 392, pp. 5865-5873, Dec 1 2013.
[20] S. D. Wu, P. H. Wu, C. W. Wu, J. J. Ding, and C. C. Wang, "Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine," Entropy, vol. 14, pp. 1343-1356, Aug 2012.
[21] R. L. Jiang, J. Chen, G. M. Dong, T. Liu, and W. B. Xiao, "The weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelop spectrum," Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, vol. 227, pp. 1116-1129, 2013.
[22] J. Q. Hu, L. B. Zhang, and W. Liang, "Dynamic degradation observer for bearing fault by MTS-SOM system," Mechanical Systems and Signal Processing, vol. 36, pp. 385-400, Apr 2013.
[23] J. Liu, W. Wang, and F. Ma, "Bearing system health condition monitoring using a wavelet cross-spectrum analysis technique," Journal of Vibration and Control, vol. 18, pp. 953-963, Jun 2012.
[24] J. Jiang and B. Zhang, "Rolling element bearing vibration modeling with applications to health monitoring," Journal of Vibration and Control, vol. 18, pp. 1768-1776, Oct 2012.
[25] F. Y. Cong, J. Chen, and G. M. Dong, "Spectral kurtosis based on AR model for fault diagnosis and condition monitoring of rolling bearing," Journal of Mechanical Science and Technology, vol. 26, pp. 301-306, Feb 2012.
[26] “Excess kurtosis” [Online] . Accessed: March 1, 2016. Available: https://www.bogleheads.org/wiki/Excess_kurtosis
[27] R. B. Randall and J. Antoni, “Rolling element bearing diagnostics—A tutorial," Mechanical Systems and Signal Processing 25(2011): 485-520, Jul 2011.
[28] C. E. Shannon, “A Mathematical Theory of Communication,” Bell System Technical Journal, vol. 27, pp. 379-423, 623-656, Jul, Oct 1948.
[29] G. E. Powell and I. C. Percival “A spectral entropy method for distinguishing regular and irregular motion of Hamiltonian systems,” Journal of Physics A: Mathematical and General, vol. 12, pp. 2053-2071, Nov 1979.
[30] O. Alter, P. O. Brown, and D. Botstein, "Singular value decomposition for genome-wide expression data processing and modeling," Proceedings of the National Academy of Sciences of the United States of America, vol. 97, pp. 10101-10106, Aug 29 2000.
[31] Y. B. Liu, Q. Long, Z. H. Feng and W. L. Liu, “Detection Method for Nonlinear and Non-stationary Signals,” Journal of Vibration and Shock, Dec 2007.
[32] R. Q Yana, Y. B. Liu, and R. X. Gao “Permutation entropy : A nonlinear statistical measure for status characterization of rotary machines,” Mechanical Systems and Signal Processing, Dec 2011.
[33] C. Bandt and B. Pompe, "Permutation entropy: A natural complexity measure for time series," Physical Review Letters, vol. 88, Apr 2002.
[34] J. Lee, H. Qiu, G. Yu, and J. Lin, "Rexnord Technical Services, Bearing Data Set, IMS, University of Cincinnati. NASA Ames Prognostics Data Repository," ed, 2007.