Installation of a Wireless and Powerless Sensing Node (WPSN) inside a spindle enables the direct transmission of monitoring signals through a metal case of a certain thickness instead of the traditional method of using connecting cables. Thus, the node can be conveniently installed inside motors to measure various operational parameters. This study extends this earlier finding by applying this advantage to the monitoring of spindle systems. After over 2 years of system observation and optimization, the system has been verified to be superior to traditional methods. The innovation of fault diagnosis in this study includes the unmatched assembly dimensions of the spindle system, the unbalanced system, and bearing damage. The results of the experiment demonstrate that the WPSN provides a desirable signal-to-noise ratio (SNR) in all three of the simulated faults, with the difference of SNR reaching a maximum of 8.6 dB. Following multiple repetitions of the three experiment types, 80% of the faults were diagnosed when the spindle revolved at 4,000 rpm, significantly higher than the 30% fault recognition rate of traditional methods. The experimental results of monitoring of the spindle production line indicated that monitoring using the WPSN encounters less interference from noise compared to that of traditional methods. Therefore, this study has successfully developed a prototype concept into a well-developed monitoring system, and the monitoring can be implemented in a spindle production line or real-time monitoring of machine tools.
References
[1]
Robinson, J.; Whelan, C.D.; Haggerty, H.K. Trends in advanced motor protection and monitoring. IEEE Trans. Ind. Appl 2004, 40, 853–860, doi:10.1109/TIA.2004.827472.
[2]
Xavier, D.; Bernard, A. Multi-agent framework based on smart sensors/actuators for machine tools control and monitoring. Eng. Appl. Artif. Intell 2006, 19, 641–655, doi:10.1016/j.engappai.2006.03.006.
[3]
Achmad, W.; Eric, Y.K.; Son, J.D.; Yang, B.S.; Andy, C.C.; Tan, A.C.C.; Gu, D.S.; Choi, B.K.; Joseph, M. Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Expert Syst. Appl 2009, 36, 7252–7261, doi:10.1016/j.eswa.2008.09.033.
[4]
Rafiee, J.; Tse, P.W.; Harifi, A.; Sadeghi, M.H. A novel technique for selecting mother wavelet function using an intelligent. Expert Syst. Appl 2009, 36, 4862–4875, doi:10.1016/j.eswa.2008.05.052.
[5]
Tseg, P.W.; Gontarz, S.; Wang, X.J. Enhanced eigenvector algorithm for recovering multiple sources of vibration signals in machine fault diagnosis. Mech. Syst. Signal Process 2007, 21, 2794–2813, doi:10.1016/j.ymssp.2007.02.010.
[6]
Xu, Z.B.; Xuan, J.P.; Shi, T.L.; Wu, B.; Hu, Y.M. Application of a modified fuzzy ARTMAP with feature-weight learning for the fault diagnosis of bearing. Expert Syst. Appl 2009, 36, 9961–9968, doi:10.1016/j.eswa.2009.01.063.
[7]
Sakthivel, N.R.; Sugumaran, V.; Babudevasenapati, S. Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Syst. Appl 2010, 9, 4040–4049.
[8]
Fan, Y.S.; Zheng, G.T. Research of high-resolution vibration signal detection technique and application to mechanical fault diagnosis. Mech. Syst. Signal Process 2007, 21, 678–687, doi:10.1016/j.ymssp.2005.10.001.
[9]
Jing, J.P.; Meng, G. A novel method for multi-fault diagnosis of rotor system. Mech. Mach. Theory 2009, 44, 697–709, doi:10.1016/j.mechmachtheory.2008.05.002.
[10]
Wua, J.D.; Mingsian, R.B.; Su, F.C.; Huang, C.W. An expert system for the diagnosis of faults in rotating machinery using adaptive order-tracking algorithm. Expert Syst. Appl 2009, 36, 5424–5431, doi:10.1016/j.eswa.2008.06.059.
[11]
Lee, D.S. Wireless and powerless sensing node system developed for monitoring motors. Sensors 2008, 8, 5005–5022, doi:10.3390/s8085005.
[12]
KXP4 Series: Accelerometers and Inclinometers; Kionix Inc: Ithaca, NY, USA, 2006.
[13]
CMSS Standard Industrial Acceleration Sensor, Available online: http://www.skf.com/files/266200.pdf (accessed on 10 October 2011).
[14]
Single Chip nRF905, Available online: http://www.nordicsemi.com/ (accessed on 10 October 2011).
