RESEARCH PAPER
Health index extracting methodology for degradation modelling and prognosis of mechanical transmissions
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School of Mechanical Engineering Beijing Institute of Technology 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
Publication date: 2019-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(1):137-144
KEYWORDS
ABSTRACT
Condition monitoring and prognosis is a key issue in ensuring stable and reliable operation of mechanical transmissions. Wear
in a mechanical transmission, which leads to the production of wear particles followed by severe wear, is a slow degradation
process that can be monitored by spectral analysis of oil, but the actual degree of degradation is often difficult to evaluate in
practical applications due to the complexity of multiple oil spectra. To solve this problem, a health index extraction methodology
is proposed to better characterize the degree of degradation compared to relying solely on spectral oil data, which leads to an
accurate estimation of the failure time when the transmission no longer fulfils its function. The health index is extracted using a
weighted average method with selection of degradation data with allocation steps for weight coefficients that lead to a reasonable
mechanical transmission degradation model. First, the degradation data used as input are selected based on source entropy which
can describe the information volume contained in each set of spectral oil data. Then, the weight coefficient of each set of degradation data is modelled by measuring the relative scale of the permutation entropy from the selected degradation data. Finally, the
selected degradation data are fused, and the health index is extracted. The proposed methodology was verified using a case study
involving a degradation dataset of multispectral oil data sampled from several power-shift steering transmissions.
REFERENCES (29)
1.
Chena Y, Tang J. Rating technical attributes in fuzzy QFD by integrating fuzzy weighted average method and fuzzy expected value operator. European Journal of Operational Research 2006; 174(3): 1553-1566,
https://doi.org/10.1016/j.ejor....
2.
Ebersbach S, Peng Z. Fault Diagnosis of Gearbox Based on Monitoring of Lubricants, Wear Debris, and Vibration. Springer US, 2013.
3.
Fan B, Li B, Feng S, Mao J, Xie Y-B. Modeling and Experimental Investigations on the Relationship between Wear Debris Concentration and Wear Rate in Lubrication Systems. Tribology International 2017; 109: 114-123,
https://doi.org/10.1016/j.trib....
4.
Foulard S, Ichchou M, Rinderknecht S, Perret-Liaudet J. Online and real-time monitoring system for remaining service life estimation of automotive transmissions—application to a manual transmission. Mechatronics 2015; 30: 140-157,
https://doi.org/10.1016/j. mechatronics.2015.06.013.
5.
Hong W, Wang S, Tomovic M M, Liu H, Shi J, Wang X. A Novel Indicator for Mechanical Failure and Life Prediction Based on Debris Monitoring. IEEE Transactions on Reliability 2017; 66(1): 161-169,
https://doi.org/10.1109/TR.201....
6.
Wakiru J M, Pintelon L, Muchiri P N, Chemweno P K. A review on lubricant condition monitoring information analysis for maintenance decision support. Mechanical Systems and Signal Processing 2019; 118: 108-132,
https://doi.org/10.1016/j.ymss....
7.
Lee J, Wu F, Zhao W, Ghaffari M, Liao L, Siegel D. Prognostics and health management design for rotary machinery systems— Reviews, methodology and applications. Mechanical Systems & Signal Processing 2014; 42(1-2): 314-334,
https://doi.org/10.1016/j. ymssp.2013.06.004.
8.
Lei Y, Li N, Guo L, Li N, Yan T, Lin J. Machinery Health Prognostics: A Systematic Review from Data Acquisition to RUL Prediction. Mechanical Systems and Signal Processing 2018; 104: 799-834,
https://doi.org/10.1016/j.ymss....
9.
Li Y, Shi J, Gong W, Zhang M. An ensemble model for engineered systems prognostics combining health index synthesis approach and particle filtering. Quality and Reliability Engineering International 2017; 33(8): 2711-2725,
https://doi.org/10.1002/qre.22....
10.
Liu K, Gebraeel N Z, Shi J. A Data-Level Fusion Model for Developing Composite Health Indices for Degradation Modeling and Prognostic Analysis. IEEE Transactions on Automation Science and Engineering 2013; 10(3): 652-664,
https://doi.org/10.1109/TASE.2....
11.
Liu L, Wang S, Liu D, Zhang Y, Peng Y. Entropy-based sensor selection for condition monitoring and prognostics of aircraft engine. Microelectronics Reliability 2015; 55(9-10): 2092-2096,
https://doi.org/10.1016/j.micr....
12.
Liu Y, Ma B, Zheng C S, Xie S Y. Failure prediction of power-shift steering transmission based on oil spectral analysis with wiener process. Spectroscopy and Spectral Analysis 2015; 35(9): 2620-2624.
13.
Luukka P. Feature selection using fuzzy entropy measures with similarity classifier. Expert Systems with Applications 2011; 38(4): 4600- 4607,
https://doi.org/10.1016/j.eswa....
14.
Man J, Zhou Q. Remaining useful life prediction for hard failures using joint model with extended hazard. Quality and Reliability Engineering International 2018,
https://doi.org/10.1002/qre.22....
15.
Murphy C K. Combining belief functions when evidence conflicts. Decision support systems 2000; 29(1): 1-9,
https://doi.org/10.1016/ S0167-9236(99)00084-6.
16.
Pecht M, Jaai R. A prognostics and health management roadmap for information and electronics-rich systems. Microelectronics Reliability 2010; 50: 317-323,
https://doi.org/10.1016/j.micr....
18.
Sheng, S. Monitoring of wind turbine gearbox condition through oil and wear debris analysis: A full-scale testing perspective. Tribology Transactions 2016; 59(1): 149-162,
https://doi.org/10.1080/104020....
19.
Si X S, Wangbde W, Zhouc D H. Remaining useful life estimation—a review on the statistical data driven approaches. European Journal of Operational Rresearch 2011; 213(1): 1-14,
https://doi.org/10.1016/j.ejor....
20.
Si X S, Wang W, Hu C H, Chen M Y, Zhou D H. A wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mechanical Systems and Signal Processing 2013; 35(1-2): 219-237,
https://doi.org/10.1016/j.ymss....
21.
Tang Y, Zhou D, Xu S, He Z. A weighted belief entropy-based uncertainty measure for multi-sensor data fusion. Sensors 2017; 17(4): 928,
https://doi.org/10.3390/s17040....
22.
Vališ D, Žák L, Pokora O. Contribution to System Failure Occurrence Prediction and to System Remaining Useful Life Estimation Based on Oil Field Data. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2015; 229(1): 36-45, https:// doi.org/10.1177/1748006X14547789.
23.
Wang Z, Hu C, Wang W, Zhou Z, Si X. A case study of remaining storage life prediction using stochastic filtering with the influence of condition monitoring. Reliability Engineering & System Safety 2014; 132: 186-195,
https://doi.org/10.1016/j.ress....
24.
Willmott C J. Some Comments on the Evaluation of Model Performance. Bulletin of the American Meteorological Society 1982; 63(11): 1309-1313,
https://doi.org/10.1175/1520-0....
25.
Wu J, Sun J, Liang L, Zha Y. Determination of weights for ultimate cross efficiency using Shannon entropy. Expert Systems withApplications 2011; 38(5): 5162-5165,
https://doi.org/10.1016/j.eswa....
26.
Yan S F, Ma B, Zheng C S. Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectral. Advances in Mechanical Engineering 2018; 10(6),
https://doi.org/10.1177/168781....
27.
Zhang Y F, Ma B, Zhao J S, Zhang H L. Fault prediction of Power-Shift Steering Transmission based on support vector regression. In Information and Automation (ICIA), 2010 IEEE International Conference, Harbin, Heilongjiang, China, 2010, June; 273-277,
https://doi. org/10.1109/ICINFA.2010.5512075.
28.
Zhu J, Yoon J M, He D, Bechhoefer E. Online particle‐contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines. Wind Energy 2015; 18(6): 1131-1149,
https://doi.org/10.1002/we.174....
29.
Zhu X, Zhong C, Zhe J. Lubricating oil conditioning sensors for online machine health monitoring—A review. Tribology International 2017; 109: 473-484,
https://doi.org/10.1016/j.trib....
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