Accelerated natural fault diagnosis in slow speed bearings with Acoustic Emission

Abstract Rolling element bearings are the most common cause of rotating machinery failure. Over the past 20 years, Acoustic Emission (AE) technology has evolved as a significant opportunity to monitor and diagnose the mechanical integrity of rolling element bearings. This paper presents results of an investigation to assess the potential of the Acoustic Emission (AE) technology for detecting and locating natural defects in rolling element bearings. To undertake this task a special purpose test-rig was built that allowed for accelerated natural degradation of a bearing race. It is concluded that sub-surface initiation and subsequent crack propagation can be detected using a range of data analysis techniques on AE’s generated from natural degrading bearings. The paper also investigates the source characterisation of AE signals associated with a defective bearing whilst in operation. This study also attempted to identify the size of a natural defect on bearings using AE technology. In conclusion, the results from this investigation show that whilst measurements on operational bearings cannot be achieved as described in this paper, the method of identifying the onset of crack propagation can be employed as a quality control tool for bearing manufacturers particularly for testing bearing material homogeneity.

[1]  T. A. Harris,et al.  Rolling Bearing Analysis , 1967 .

[2]  Claude E. Shannon,et al.  A Mathematical Theory of Communications , 1948 .

[3]  Asok K. Nanda,et al.  Rényi information measure for a used item , 2007, Inf. Sci..

[4]  M. Elforjani,et al.  Natural mechanical degradation measurements in slow speed bearings , 2009 .

[5]  S.M. Kay,et al.  Spectrum analysis—A modern perspective , 1981, Proceedings of the IEEE.

[6]  Serhan Ozdemir,et al.  Measures of uncertainty in power split systems , 2007 .

[7]  J. Sethna Statistical Mechanics: Entropy, Order Parameters, and Complexity , 2021 .

[8]  D Mba,et al.  Condition monitoring of slow-speed rolling element bearings using stress waves , 2001 .

[9]  D. Mba,et al.  Acoustic Emission Waveform Changes for Varying Seeded Defect Sizes , 2006 .

[10]  Kalman Žiha,et al.  Event oriented system analysis , 2000 .

[11]  R. Such,et al.  Estimation of bearing defect size with acoustic emission , 2004 .

[12]  David,et al.  A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size , 2006 .

[13]  Arthur W. Lees,et al.  Detection of severe sliding and pitting fatigue wear regimes through the use of broadband acoustic emission , 2005 .

[14]  A. Palmgren Ball and roller bearing engineering , 1945 .

[15]  David Mba,et al.  Observations and Location of Acoustic Emissions for a Naturally Degrading Rolling Element Thrust Bearing , 2008 .

[16]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[17]  M. Elforjani,et al.  Monitoring the Onset and Propagation of Natural Degradation Process in a Slow Speed Rolling Element Bearing With Acoustic Emission , 2008 .

[18]  David Mba,et al.  Development of Acoustic Emission Technology for Condition Monitoring andDiagnosis of Rotating Machines; Bearings, Pumps, Gearboxes, Engines and RotatingStructures. , 2006 .

[19]  Jay Lee,et al.  Enhanced diagnostic certainty using information entropy theory , 2003, Adv. Eng. Informatics.

[20]  N. Tandon,et al.  Application of acoustic emission technique for the detection of defects in rolling element bearings , 2000 .

[21]  D Mba,et al.  Bearing defect diagnosis and acoustic emission , 2003 .

[22]  M. P. Norton,et al.  Fundamentals of Noise and Vibration Analysis for Engineers , 1990 .

[23]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .