146
Z. Wang et al.
13.4 Conclusion
In this paper, conventional double emission index is improved. It overcomes the
shortcomings of conventional trend analysis with AE indexes, such as the introduction of subjective error, the large amount of displayed data, the unclear distinction
of chaotic stages and the inability to achieve a good correspondence with statistical
indexes. Trend analysis with multi-index from the collected AE data of ball bearings
life-cycle is carried out. The results show that, the improved AE index can give a
better fatigue evolution information. The display data is simplified and the stage identification accuracy is improved. Finally, the characteristics and evolution laws of the
corresponding indexes from each fatigue process are summarized. A new reference
basis for judging the fatigue process of ball bearing is provided.
Acknowledgements The research work is supported by the Education Department Foundation of Yunnan Province (Grant No. 2018JS025) and Provincial Education Program (Grant No.
KKSY201701018).
References
1. J. Chen, Mechanical Equipment Fault Diagnosis Technology and Its Application. Shanghai
Higher Education Electronic Audio and Video Publishing House (2003)
2. T.A. Harris, M.N. Kotzalas, Rolling Bearing Analysis (2010)
3. T. Yoshioka, T. Fujiwara, Application of acoustic emission technique to detection of rolling
bearing failure. Am. Soc. Mech. Eng. 14, 55–76 (1984)
4. A.M. Al-Ghamd, D. Mba, A comparative experimental study on the use of acoustic emission
and vibration analysis for bearing defect identification and estimation of defect size. Mech.
Syst. Signal Process. 20(7), 1537–1571 (2006)
5. W. Achmad, Y.K. Eric, S. Jong-Duk et al., Fault diagnosis of low speed bearing based on
relevance vector machine and support vector machine. Exp. Syst. Appl. Int. J. Arch. 3(36),
7252–7261 (2009)
6. T. Yoshioka, T. Fujiwara, Paper II(i) “Measurement of propagation initiation and propagation
time of rolling contact fatigue cracks by observation of acoustic emission and vibration”.
Tribology 12(2–3), 29–33 (1987)
7. M. Elforjani, D. Mba, Monitoring the onset and propagation of natural degradation process in a
slow speed rolling element bearing with acoustic emission. J. Vib. Acoust. 130(4), 1257–1261
(2008)
8. M. Elforjani, D. Mba, Observations and location of acoustic emission for a naturally degrading
rolling element thrust bearing. J. Fail. Anal. Prev. 8(4), 370–385 (2008)
9. M. Elforjani, D. Mba, Natural mechanical degradation measurements in slow speed bearings.
Eng. Fail. Anal. 16(1), 521–532 (2009)
10. A.W. Warren, Y.B. Guo, Acoustic emission monitoring for rolling contact fatigue of super
finished ground surfaces. Int. J. Fatigue 29, 603–614 (2007)
11. Z. Rahman, H. Ohba, T. Yoshioka et al., Incipient damage detection and its propagation
monitoring of rolling contact fatigue by acoustic emission. Tribol. Int. 42, 807–815 (2009)
12. H.P. Filip et al., Monitoring of acoustic emission signal of loaded axial bearings. J. Mater. Sci.
Eng. A1(5), 717–724 (2011)
Z. Wang et al.
13.4 Conclusion
In this paper, conventional double emission index is improved. It overcomes the
shortcomings of conventional trend analysis with AE indexes, such as the introduction of subjective error, the large amount of displayed data, the unclear distinction
of chaotic stages and the inability to achieve a good correspondence with statistical
indexes. Trend analysis with multi-index from the collected AE data of ball bearings
life-cycle is carried out. The results show that, the improved AE index can give a
better fatigue evolution information. The display data is simplified and the stage identification accuracy is improved. Finally, the characteristics and evolution laws of the
corresponding indexes from each fatigue process are summarized. A new reference
basis for judging the fatigue process of ball bearing is provided.
Acknowledgements The research work is supported by the Education Department Foundation of Yunnan Province (Grant No. 2018JS025) and Provincial Education Program (Grant No.
KKSY201701018).
References
1. J. Chen, Mechanical Equipment Fault Diagnosis Technology and Its Application. Shanghai
Higher Education Electronic Audio and Video Publishing House (2003)
2. T.A. Harris, M.N. Kotzalas, Rolling Bearing Analysis (2010)
3. T. Yoshioka, T. Fujiwara, Application of acoustic emission technique to detection of rolling
bearing failure. Am. Soc. Mech. Eng. 14, 55–76 (1984)
4. A.M. Al-Ghamd, D. Mba, A comparative experimental study on the use of acoustic emission
and vibration analysis for bearing defect identification and estimation of defect size. Mech.
Syst. Signal Process. 20(7), 1537–1571 (2006)
5. W. Achmad, Y.K. Eric, S. Jong-Duk et al., Fault diagnosis of low speed bearing based on
relevance vector machine and support vector machine. Exp. Syst. Appl. Int. J. Arch. 3(36),
7252–7261 (2009)
6. T. Yoshioka, T. Fujiwara, Paper II(i) “Measurement of propagation initiation and propagation
time of rolling contact fatigue cracks by observation of acoustic emission and vibration”.
Tribology 12(2–3), 29–33 (1987)
7. M. Elforjani, D. Mba, Monitoring the onset and propagation of natural degradation process in a
slow speed rolling element bearing with acoustic emission. J. Vib. Acoust. 130(4), 1257–1261
(2008)
8. M. Elforjani, D. Mba, Observations and location of acoustic emission for a naturally degrading
rolling element thrust bearing. J. Fail. Anal. Prev. 8(4), 370–385 (2008)
9. M. Elforjani, D. Mba, Natural mechanical degradation measurements in slow speed bearings.
Eng. Fail. Anal. 16(1), 521–532 (2009)
10. A.W. Warren, Y.B. Guo, Acoustic emission monitoring for rolling contact fatigue of super
finished ground surfaces. Int. J. Fatigue 29, 603–614 (2007)
11. Z. Rahman, H. Ohba, T. Yoshioka et al., Incipient damage detection and its propagation
monitoring of rolling contact fatigue by acoustic emission. Tribol. Int. 42, 807–815 (2009)
12. H.P. Filip et al., Monitoring of acoustic emission signal of loaded axial bearings. J. Mater. Sci.
Eng. A1(5), 717–724 (2011)