Development of Dynamic Signal Analyzer Virtual Instrument (DSA VI): A Research Proposal

Asmara Yanto, Anrinal Anrinal


At present, one of the maintenance types that is being developed is the predictive maintenance based on the mechanical signals obtained by performing the mechanical quantities measurements. In general, a mechanical signal is a dynamic signal where to acquire this signal, it is required a dynamic signal analyzer (DSA) instrument.  However, the availability of DSA instruments in the market is limited in functionality and specification and also high cost. Therefore, in this work, a DSA instrument in the form of computer-based virtual instrument (DSA VI) would be developed. The DSA VI would designed by using the LabVIEW software and an Arduino UNO hardware. It is hopefully that the developed DSA VI capable to acquiring, processing, displaying, storing and reading the measured mechanical signals.


predictive maintenance, dynamic signal analyzer, computer-based virtual instrument, measured mechanical signals

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S.P. Mogal and D.I. Lalwani, “A brief review on fault diagnosis of rotating machineries,” Applied Mechanics and Materials, 2014, vol. 541-542, pp. 635-640.

P. Gupta and O.P. Gandi, “Cost-down time monitoring for defect detection in rotating equipment,” International Journal of Performability Engineering, 2014, vol. 10(2), pp. 197-210.

M. Saxena, O.O. Bannett, V. Sharma and R. Khemchandani, “Fault prediction in ball bearing by using analytical wavelet transform (AWT),” International Journal of Scientific and Engineering Research, 2014, vol. 658, pp. 289-294.

S. Khanam, N. Tandon and J.K. Dutt, “Fault size estimation in the outer race of ball bearing using discrete wavelet transform of the vibration signal,” The 2nd International Conference on Innovations in Automation and Mechatronics Engineering (ICIAME), 2014, pp. 12-19.

B. Carmen and B. Florin, “Bearing scuffing detection and condition monitoring using virtual instrumentation,” Applied Mechanics and Materials, 2014, vol. 657, pp. 604-608.

A.J. Kumbhar and N.K. Chhapkhane, “Detection of the distributed defects on Inner and outer race of ball bearing using vibration analysis,” International Journal of Engineering Research and Technology (IJERT), 2014, vol. 3(11), pp. 147-150.

B. Carmen, M. Razvan and O.N. Dumitru, “Study on the defects size of ball bearings elements using vibration analysis,” Applied Mechanics and Materials, 2014, vol. 658, pp. 289-294.

W. Shuqian, M. Mei, Z. Jingling, Z. Weinan and W. Guoqing, “Vibration test of bearing ball fatigue testing machine base on VB,” Applied Mechanics and Materials, 2014, vol. 607, pp. 523-526.

D.S. Shah and V.N. Patel, “A review of dynamic modeling and fault identifications methods for rolling element bearing,” in The 2nd International Conference on Innovations in Automation and Mechatronics Engineering (ICIAME), 2014, pp. 447-456.

M. Yuzukirmizi and H. Arslan, “Fault diagnosis of shaft-ball bearing system using one-way analysis of variance,” Mathematical and Computational Applications, 2014, vol. 19(1), pp. 37-49.

C.H. Chen, R.J. Shyu and C.K. Ma, “A new fault diagnosis method of rotating machinery,” Shock and Vibration, 2008, vol. 15, pp. 585-598.

H. Yang, J. Mathew and L. Ma, “Intelligent Diagnosis of Rotating Machinery Faults-A Review,” in The 3rd Asia-Pacific Conference on Systems Integrity and Maintenance (ACSIM), 2002, 25-27 September 2002, Cairns, Australia.

E. Swanson, C.D. Powel and S. Weissman, “A practical Review of Rotating Machinery Critical Speeds and Modes,” Sound and Vibration, 2005, vol. 162(3), pp. 471-487.

S.H. Ghafari, A Fault Diagnosis System for Rotary Machinery Supported by Rolling Element Bearings, 2007, University of Waterloo: PhD theses.

S.H. Ghafari, F. Golnaraghi and F. Ismail, “Fault diagnosis based on chaotic vibration of rotor systems Supported by Ball Bearings,” in The Proceeding of COMADEM, 2006, pp. 819-826.

F.K. Choy, J. Zhou, M.J. Braun and L. Wang, “Vibration monitoring and damage quantification of faulty ball bearings,” Tribology, 2005, vol. 127(4), pp. 776-783.

T. Williams, X. Ribadeneira, S. Billington and T. Kurfesss, “Rolling element bearing diagnostics in run-to-failure lifetime testing,” Mechanical Systems and Signal Processing, 2001, vol. 15(5), pp. 979-993.

B. Mevel and J.L. Guyader, “Routes to chaos in ball bearings,” Sound and Vibration, 1993, vol. 162(3), pp. 471-487.

D. Kanneg and W. Wang, “A Wavelet Spectrum Technique for Machinery Fault Diagnosis,” Journal of Signal and Information Processing, 2011, vol. 2, pp. 322-329.

A. Yoshihiro, M. Satoru, and K. Shinji, “Online Monitoring Technologyby nalysis of Highly Accurate Vibration Waveform to Diagnose Abnormality of Machines,” JFE Technical Report, 2012, vol. 17, pp. 17–22.

A. Yoshihiro, M. Satoru, and K. Shinji, “Multi-function Online Monitoring (Condition-eye),” JFE Giho, 2011, vol. 27, pp. 58–60.

W. Niu, “Fault diagnosis for rotator in rotating machinery based on support vector machine,” Applied Mechanics and Materials, 2014, vol. 532, pp. 102-105.

C. Yue, X. Ren, Y. Yang and W. Deng, “Unbalance Identification of Speed-Variant Rotary Machinery without Phase Angle Measurement,” Shock and Vibration, 2015, vol. 62(3), pp. 463-471.


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