Volume 03 Issue 06-2023
22
American Journal Of Applied Science And Technology
(ISSN
–
2771-2745)
VOLUME
03
ISSUE
06
Pages:
22-25
SJIF
I
MPACT
FACTOR
(2021:
5.
705
)
(2022:
5.
705
)
(2023:
7.063
)
OCLC
–
1121105677
Publisher:
Oscar Publishing Services
Servi
ABSTRACT
Rolling element bearings are critical components in many industrial applications, and their failure can lead to
significant downtime and maintenance costs. Therefore, predicting the remaining useful life of bearings is essential
for effective maintenance scheduling and avoiding unplanned downtime. In this study, vibration fluctuation analysis
and failure modes investigation were employed to determine the failure threshold of rolling element bearings. Results
showed that the vibration fluctuation of bearings increased significantly when the bearing was close to failure. The
failure modes of bearings were also identified, and the corresponding vibration signals were analyzed. Based on these
findings, a failure threshold was determined, which can be used to predict the remaining useful life of bearings.
KEYWORDS
Rolling element bearings, vibration fluctuation analysis, failure modes investigation, failure threshold, remaining
useful life.
INTRODUCTION
Research Article
FAILURE THRESHOLD DETERMINATION OF ROLLING ELEMENT
BEARINGS: VIBRATION FLUCTUATION ANALYSIS AND FAILURE MODES
INVESTIGATION
Submission Date:
June 02, 2023,
Accepted Date:
June 07, 2023,
Published Date:
June 12, 2023
Crossref doi:
https://doi.org/10.37547/ajast/Volume03Issue06-05
Sajjad Behzad
School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
Mehdi Addin Arghand
Engineering Department, University of Zanjan, Zanjan, Iran
Journal
Website:
https://theusajournals.
com/index.php/ajast
Copyright:
Original
content from this work
may be used under the
terms of the creative
commons
attributes
4.0 licence.
Volume 03 Issue 06-2023
23
American Journal Of Applied Science And Technology
(ISSN
–
2771-2745)
VOLUME
03
ISSUE
06
Pages:
22-25
SJIF
I
MPACT
FACTOR
(2021:
5.
705
)
(2022:
5.
705
)
(2023:
7.063
)
OCLC
–
1121105677
Publisher:
Oscar Publishing Services
Servi
Rolling element bearings are widely used in various
industrial applications, such as electric motors,
turbines, and gearboxes. These bearings play a critical
role in the proper functioning of machines and
equipment. However, the failure of bearings can lead
to significant downtime and maintenance costs.
Therefore, predicting the remaining useful life of
bearings is essential for effective maintenance
scheduling and avoiding unplanned downtime. Many
techniques have been developed to predict the
remaining useful life of bearings, including vibration
analysis,
acoustic
emission,
and
temperature
monitoring. Among these techniques, vibration
analysis is one of the most commonly used methods
due to its effectiveness and ease of implementation. In
this study, vibration fluctuation analysis and failure
modes investigation were employed to determine the
failure threshold of rolling element bearings.
METHOD
The study involved the following steps:
Selection of rolling element bearings: Several rolling
element bearings were selected from different
industrial applications, including electric motors,
turbines, and gearboxes.
Experimental setup:
The experimental setup involved mounting the
bearings on a test rig and subjecting them to various
loads and speeds. The vibration signals were recorded
using an accelerometer attached to the bearing
housing.
Vibration fluctuation analysis:
The vibration signals were analyzed using the vibration
fluctuation method to determine the failure threshold
of the bearings. The vibration fluctuation is a measure
of the deviation of the vibration signal from its mean
value.
Failure modes investigation:
The bearings were inspected after the tests to identify
the failure modes. The corresponding vibration signals
were analyzed to determine the relationship between
the vibration signals and failure modes.
Failure threshold determination:
Based on the results of the vibration fluctuation
analysis and failure modes investigation, a failure
threshold was determined for each type of bearing.
RESULTS
The results showed that the vibration fluctuation of
bearings increased significantly when the bearing was
close to failure. The failure modes of bearings were
also identified, including fatigue spalling, plastic
deformation, and wear. The corresponding vibration
signals were analyzed, and the relationship between
the vibration signals and failure modes was
determined. Based on these findings, a failure
threshold was determined for each type of bearing,
which can be used to predict the remaining useful life
of bearings.
DISCUSSION
The vibration fluctuation analysis and failure modes
investigation proved to be effective in determining the
failure threshold of rolling element bearings. The
identified failure modes can be used to develop
strategies for preventing bearing failure, such as
improving lubrication and reducing loads. The failure
threshold can also be used to predict the remaining
useful life of bearings and schedule maintenance
activities accordingly.
Volume 03 Issue 06-2023
24
American Journal Of Applied Science And Technology
(ISSN
–
2771-2745)
VOLUME
03
ISSUE
06
Pages:
22-25
SJIF
I
MPACT
FACTOR
(2021:
5.
705
)
(2022:
5.
705
)
(2023:
7.063
)
OCLC
–
1121105677
Publisher:
Oscar Publishing Services
Servi
CONCLUSION
In conclusion, this study demonstrated that vibration
fluctuation analysis and failure modes investigation
can be employed to determine the failure threshold of
rolling element bearings. The identified failure modes
and failure threshold can be used to develop strategies
for preventing bearing failure and predicting the
remaining useful life of bearings. This approach can
lead to improved machine reliability, reduced
maintenance.
REFERENCES
1.
Mahamad, A.K.; Saon, S.; Hiyama, T. Predicting
remaining useful life of rotating machinery based
artificial neural network. Comput. Math. Appl.
2010,
60,
1078
–
1087.
Scholar]
[CrossRef][Green Version]
2.
ISO. Standard ISO 13381-1. Condition Monitoring
and Diagnostics of Machines
—
Prognostics
—
Part
1: General Guidelines; ISO: Geneva, Switzerland,
2015. [Google Scholar]
3.
ISO. Standard ISO 10816-3. Mechanical Vibration
—
Evaluation of Machine Vibration by Measurements
on Non-Rotating Parts
—
Part 3: Industrial
Machines with Normal Power above 15 kW and
Nominal Speeds between 120 r/min and 15000
r/min; ISO: Geneva, Switzerland, 2009. [Google
Scholar]
4.
Elasha, F.; Shanbr, S.; Li, X.; Mba, D. Prognosis of a
wind turbine gearbox bearing using supervised
machine learning. Sensors 2019, 19, 3092. [Google
Scholar] [CrossRef] [PubMed][Green Version]
5.
Wang, D.; Tsui, K.L. Two novel mixed effects
models for prognostics of rolling element bearings.
Mech. Syst. Signal Process. 2018, 99, 1
–
13. [Google
Scholar] [CrossRef]
6.
Wang, Y.; Peng, Y.; Zi, Y.; Jin, X.; Tsui, K.L. A Two-
Stage Data-Driven-Based Prognostic Approach for
Bearing Degradation Problem. IEEE Trans. Ind.
Inform. 2016, 12, 924
–
932. [Google Scholar]
[CrossRef]
7.
Lim, C.K.R.; Mba, D. Switching Kalman filter for
failure prognostic. Mech. Syst. Signal Process. 2015,
52, 426
–
435. [Google Scholar] [CrossRef]
8.
Bastami, A.R.; Aasi, A.; Arghand, H.A. Estimation of
remaining useful life of rolling element bearings
using wavelet packet decomposition and artificial
neural network. IJSTE 2019, 43, 233
–
245. [Google
Scholar] [CrossRef]
9.
Nectoux, P.; Gouriveau, R.; Medjaher, K.; Ramasso,
E.; Chebel-Morello, B.; Zerhouni, N.; Varnier, C.
PRONOSTIA: An experimental platform for
bearings accelerated degradation tests. In
Proceedings of the IEEE International Conference
on Prognostics and Health Management, Denver,
CO, USA, 18
–
21 June 2012; Available online:
https://hal.archives-ouvertes.fr/hal-00719503
(accessed on 20 July 2012).
10.
Behzad, M.; Arghand, H.A.; Bastami, A.R.
Remaining useful life prediction of ball-bearings
based on high-frequency vibration features. Proc.
Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 2018, 232,
3224
–
3234. [Google Scholar] [CrossRef]
11.
Behzad, M.; Arghand, H.A.; Bastami, A.R. Rolling
Element Bearings Prognostics Using High-
Frequency Spectrum of Offline Vibration Condition
Monitoring Data. In Proceedings of the Condition
Monitoring
and
Diagnostic
Engineering
Management, Rustenburg, South Africa, 2
–
5 July
2018. [Google Scholar]
12.
Li, N.; Lei, Y.; Lin, J.; Ding, S.X. An improved
exponential model for predicting remaining useful
life of rolling element bearings. IEEE Trans. Ind.
Electron. 2015, 62, 7762
–
7773. [Google Scholar]
[CrossRef]
13.
Wang, P.; Coit, D.W. Reliability and degradation
modeling with random or uncertain failure
Volume 03 Issue 06-2023
25
American Journal Of Applied Science And Technology
(ISSN
–
2771-2745)
VOLUME
03
ISSUE
06
Pages:
22-25
SJIF
I
MPACT
FACTOR
(2021:
5.
705
)
(2022:
5.
705
)
(2023:
7.063
)
OCLC
–
1121105677
Publisher:
Oscar Publishing Services
Servi
threshold. In Proceedings of the 2007 Annual
Reliability
and
Maintainability
Symposium,
Orlando, FL, USA, 22
–
25 January 2007. [Google
Scholar] [CrossRef]
14.
Nystad, B.H.; Gola, G.; Hulsund, J.E. Lifetime
models for remaining useful life estimation with
randomly distributed failure thresholds. In
Proceedings of the First European Conference of
the Prognostics and Health Management Society,
2012;
Available
online:
https://www.phmsociety.org/node/788 (accessed
on 30 May 2012).
15.
Yu, T.; Fuh, C.D. Estimation of Time to Hard Failure
Distributions Using a Three-Stage Method. IEEE
Trans. Reliab. 2010, 59, 405
–
412. [Google Scholar]
[CrossRef]
16.
Qiu, H.; Lee, J.; Lin, J.; Yu, G. Wavelet Filter-based
Weak Signature Detection Method and its
Application on Roller Bearing Prognostics. J. Sound
Vib. 2006, 289, 1066
–
1090. [Google Scholar]
[CrossRef]
17.
Tobon-Mejia, D.A.; Medjaher, K.; Zerhouni, N.;
Tripot, G. A Data-Driven Failure Prognostics
Method Based on Mixture of Gaussians Hidden
Markov Models. IEEE Trans. Reliab. 2012, 61, 491
–
503. [Google Scholar] [CrossRef][Green Version]
18.
Cui, L.; Wang, X.; Xu, Y.; Jiang, H.; Zhou, J. A novel
switching unscented Kalman filter method for
remaining useful life prediction of rolling bearing.
Measurement 2019, 135, 678
–
684. [Google
Scholar] [CrossRef]
19.
Behzad, M.; Arghand, H.A.; Bastami, A.R.; Zuo, M.J.
Prognostics of rolling element bearings with the
combination of Paris law and reliability method. In
Proceedings of the 2017 Prognostics and System
Health Management Conference, Harbin, China, 9
–
12 July 2017. [Google Scholar] [CrossRef]
