Analysis of Acoustic Emission Data for Bearings subject to Unbalance



Published Nov 1, 2020
Seyed A. Niknam Tomcy Thomas J. Wesley Hines Rapinder Sawhney


Acoustic Emission (AE) is an effective nondestructive method for investigating the behavior of materials under stress. In recent decades, AE applications in structural health monitoring have been extended to other areas such as rotating machineries and cutting tools. This research investigates the application of acoustic emission data for unbalance analysis and detection in rotary systems. The AE parameter of interest in this study is a discrete variable that covers the significance of count, duration and amplitude of AE signals. A statistical model based on Zero-Inflated Poisson (ZIP) regression is proposed to handle over-dispersion and excess zeros of the counting data. The ZIP model indicates that faulty bearings can generate more transient wave in the AE waveform. Control charts can easily detect the faulty bearing using the parameters of the ZIP model. Categorical data analysis based on generalized linear models (GLM) is also presented. The results demonstrate the significance of the couple unbalance.

Abstract 89 | PDF Downloads 273



acoustic emission, Count Data, Categorical data analysis, Poisson regression, Rolling element bearing, Unbalance

Tan, C., Irving, P., Mba, D. (2007). A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears. Mechanical Systems and Signal Processing, vol. 21, pp. 208-233.
Alghamd, A., & Mba, D. (2006). A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mechanical Systems and Signal Processin, vol. 20, pp. 1537-1571.
Tandon, N., & Mata, S. (1999). Detection of Defects in Gears by Acoustic Emission Measurements. Journal of Acoustic Emission, vol. 17, pp. 23-27.
Eftekharnejad, B., & Mba, M. (2009). Seeded fault detection on helical gears with acoustic emission. Applied Acoustics, vol. 70, pp. 547-555.
Mba, D., & Rao, R. B. K. N. (2006). Development of Acoustic Emission Technology for Condition Monitoring and Diagnosis of Rotating Machines; Bearings, Pumps, Gearboxes, Engines and Rotating Structures. The Shock and Vibration Digest, vol. 38, pp. 3-16.
Yoshioka, T., & Fujiwara, T., (1982). New acoustic emission source locating system for the study of rolling contact fatigue. Wear, vol. 81, pp. 183-186.
Niknam, S. A. & Liao, H. (2011). Diagnostic and Prognostic Modeling of High-Speed Milling Machine Cutters. The 7th International Conference on Mathematical Methods in Reliability - Theory, Methods, Applications, Beijing, China.
PAC (Physical Acoustic Corporation) (2007). PCI-2 Based AE System. Princeton Jct, NJ
Miyachika, K., Oda, S., Koide, T. (1995). Acoustic Emission of Bending Fatigue Process of Spur Gear Teeth. Journal of Acoustic Emission, vol. 13, pp. 47-53.
He, D., Li, R., Bechhoefer, E. (2010). Split Torque Type Gearbox Fault Detection using Acoustic Emission and Vibration Sensors. International Conference on Networking Sensing and Control.
Bansal, V., Gupta, B. C., Prakash, A., Eshwar, V. A. (1990). Quality inspection of rolling element bearing using acousic emission technique. Acoustic Emission, vol. 9, pp. 142-146.
Choudhury, A. & Tandon, N. (2000). Application of acoustic emission technique for the detection of defects in rolling element bearings. Tribology International , vol. 33, pp. 39-45.
Lu, B., Y. Li, Wu, X., Yang, Z. ( 2009). A review of recent advances in wind turbine condition monitoring and fault diagnosis. Power Electronics and Machines in Wind Applications, Milwaukee, WI.
Hyers, R. W., McGowan, J. G., Sullivan, K.L., Manwell, J.F., Syrett, B.C. (2006). Condition monitoring and prognosis of utility scale wind turbines. Energy Materials, vol. 1, pp. 187-203.
Hameed, Z., Hong, Y.S., Cho, Y.M., Ahn, S.H., Song, C.K. (2009). "Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable Energy Reviews, vol. 13, pp. 1-39.
Tandon, N., & Nakra, B. C. (1990). Defect Detection of Rolling Element Bearings by Acoustic Emission Method. Journal of Acoustic Emission, vol. 9, pp. 25-28.
Morhain, A., & Mba, D. (2003). Bearing defect diagnosis and acoustic emission. Engineering Tribology, vol. 217, pp. 257-272.
X. Li (2002). A brief review- acoustic emission method for tool wear monitoring during turning. International Journal of Machine Tools & Manufacture, vol. 42, pp. 157-165.
Sharma, V.S., Sharma, S. K., Sharma, A. K. (2007). An approach for condition monitoring of a turning tool. Journal of Engineering Manufacture, vol. 22, pp. 635-646.
Carpinteri, A., Lacidogna, G., Pugno, N. (2007). Structural damage diagnosis and life-time assessment by acoustic emission monitoring. Engineering Fracture Mechanics, vol. 74, pp. 273-289.
Liu, W.S., & Cela, J. (2008). Count Data Models in SAS. SAS Global Forum.
Lambert, D. (1992). Zero-Inflated Poissoin Regression, with an application to defects in manufacturing. Technometrics, vol. 34, pp. 1-14.
Guikema, S. D. & J. P. Coffelt (2008). Modeling count data in risk analysis and reliability engineering. In K. B. Misra (ed.). Handbook of Performability Engineering,. London, Springer.
Wowk V. (1995). Machinery Vibration: Balancing. New York, McGraw-Hill.
He, S., Huang, W., Woodall, W.H . (2011). CUSUM Charts for Monitoring a Zero-inflated Poisson Process. Quality and Reliability Engineering International, vol. 28, pp. 181-192.
Montgomery, D. C. (2001). Introduction to Statistical Quality Control, 4th ed. John Wiley & Sons, New York.
Leger, R.P., W.J. Garland, and W.F.S. Poehlman, (1998). Fault detection and diagnosis using statistical control charts and artificial neural networks. Artificial Intelligence in Engineering, vol. 12, pp. 35-47.
Technical Papers