Physics-based intelligent prognosis for rolling bearing with fault feature extraction
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Successful condition monitoring of rotating machinery relies on the accurate prediction of the remaining life of the rotating components. This paper proposes a physics-based prognostic model for rolling element bearings using the realized volatility (RV) and wavelet neural network (WNN) to predict the remaining life of bearings. The proposed method overcomes the difficulties of forecasting the failure of bearings under various degradation patterns and improves the accuracy of prediction. The faulty signal is extracted using an AR filter to reveal the degradation trend. The prognosis is performed by calculating the RV and energy ratio, which are used to identify the abnormality of vibration in the system and degradation patterns before catastrophic failure occurs. Then, the WNN incorporated with physical inputs from the properties of the bearings predicts the failure cycles. The prediction yields a satisfactory result between the experimental and predicted failure cycle. The prognostic accuracy of the physics-based WNN model is compared with a widely used time-series model, the auto-regressive integrated moving average (ARIMA) model. The WNN model has an advantage of reducing error over the ARIMA model in various degradation patterns and overcomes the disadvantage of models requiring additional data after significant damage is present in the bearings.
KeywordsBall bearing Bearing degradation trend prognosis Forecasting Prognostics and health management Realized volatility Wavelet neural network
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Y.L. and Q.L. created the model and analyzed the data; S.Y.L. provided feedback of the concept; Y.L. wrote the paper.
- 1.Group, W MR (1985) Report of large motor reliability survey of industrial and commercial installations, Part I. IEEE Trans Ind Appl 21(4):853–864Google Scholar
- 2.Kotzalas MN, Harris TA (2001) Fatigue failure progression in ball bearings. Trans Am Soc Mech Eng J Tribol 123(2):238–242Google Scholar
- 3.Tong W (2014) Mechanical design of electric motors. CRC pressGoogle Scholar
- 4.Graney BP, Starry K (2012) Rolling element bearing analysis. Mater Eval 70 (1)Google Scholar
- 5.Lundberg G, Palmgren A (1949) Dynamic capacity of rolling bearings. J Appl Mech Trans ASME 16(2):165–172Google Scholar
- 7.Li Y (1999) Dynamic prognostics of rolling element bearing condition. Georgia Institute of TechnologyGoogle Scholar
- 15.Corsi F (2004) A simple long memory model of realized volatilityGoogle Scholar
- 21.Berenji HR, Wang Y (2006) In Wavelet neural networks for fault diagnosis and prognosis, IEEE Int Conf Fuzzy Syst; pp 1334–1339Google Scholar
- 23.Vachtsevanos G, Wang P (2001) In Fault prognosis using dynamic wavelet neural networks, AUTOTESTCON Proceedings, 2001 I.E. Systems Readiness Technology Conference, IEEE: pp 857–870Google Scholar
- 25.Harris TA (2001) Rolling bearing analysis. John Wiley and sonsGoogle Scholar
- 27.Eshel G The yule walker equations for the AR coefficientsGoogle Scholar
- 28.Anderson TL (2017) Fracture mechanics: fundamentals and applications. CRC pressGoogle Scholar
- 31.Nectoux P, Gouriveau R, Medjaher K, Ramasso E, Chebel-Morello B, Zerhouni N, Varnier C (2012) In PRONOSTIA: an experimental platform for bearings accelerated degradation tests, IEEE International Conference on Prognostics and Health Management, PHM'12., IEEE catalog number: CPF12PHM-CDR: pp 1–8Google Scholar