An Optimal Lubrication Oil Replacement Method Based on Selected Oil Field Data

The regular replacement of lubricating oil plays a key role in improving machine reliability and reducing unexpected failures of an oil lubricated system. This paper proposes a condition-based maintenance problem with selected oil field data to determine the optimal time of the lubricating oil replacement. The selected oil field data contain health information about the lubricating oil, so the degradation state of the oil can be predicted and the future health condition can be evaluated. The proposed lubricating oil replacement problem is modeled with the evaluated oil health condition in a Markov decision process framework and then, a method for constructing a health index for the lubricating oil is proposed based on information theory to fuse the multiple oil field data and build a degradation progression prediction model. Finally, the proposed method for condition-based lubricating oil replacement is illustrated in a practical case study. The possible applications of an optimal policy for lubricating oil replacement are much wider. For instance, the method can be used as an input to optimize an operational plan and further reduce the maintenance costs.

[1]  David Valis,et al.  Failure prediction of diesel engine based on occurrence of selected wear particles in oil , 2015 .

[2]  Shuangwen Sheng,et al.  Monitoring of Wind Turbine Gearbox Condition through Oil and Wear Debris Analysis: A Full-Scale Testing Perspective , 2016 .

[3]  Biao Ma,et al.  Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectra , 2018, Advances in Mechanical Engineering.

[4]  Peter Kipruto Chemweno,et al.  A review on lubricant condition monitoring information analysis for maintenance decision support , 2019, Mechanical Systems and Signal Processing.

[5]  Qi Zhang,et al.  An Optimal Condition-Based Replacement Method for Systems With Observed Degradation Signals , 2018, IEEE Transactions on Reliability.

[6]  Biao Ma,et al.  Health index extracting methodology for degradation modelling and prognosis of mechanical transmissions , 2018 .

[7]  Nan Chen,et al.  Condition-based maintenance using the inverse Gaussian degradation model , 2015, Eur. J. Oper. Res..

[8]  Wan Yao.  THRESHOLD PROBLEMS ON FAULT DIAGNOSIS OF THE ATOMIC EMISSION SPECTROMETER OIL ANALYSIS , 2006 .

[9]  Wenbin Wang,et al.  A case study of condition based maintenance modelling based upon the oil analysis data of marine diesel engines using stochastic filtering , 2012 .

[10]  Wen Jiang,et al.  An evidential sensor fusion method in fault diagnosis , 2016 .

[11]  Soumaya Yacout,et al.  Parameter Estimation Methods for Condition-Based Maintenance With Indirect Observations , 2010, IEEE Transactions on Reliability.

[12]  Yu Peng,et al.  Entropy-based sensor selection for condition monitoring and prognostics of aircraft engine , 2015, Microelectron. Reliab..

[13]  Qiang Miao,et al.  Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators , 2018, IEEE Access.

[14]  Pasi Luukka,et al.  Feature selection using fuzzy entropy measures with similarity classifier , 2011, Expert Syst. Appl..

[15]  Shuai Xu,et al.  A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion , 2017, Sensors.

[16]  Eric Bechhoefer,et al.  Online condition monitoring and remaining useful life prediction of particle contaminated lubrication oil , 2013, 2013 IEEE Conference on Prognostics and Health Management (PHM).

[17]  Dong Wang,et al.  Brownian motion with adaptive drift for remaining useful life prediction: Revisited , 2018 .

[18]  Jie Wu,et al.  Determination of weights for ultimate cross efficiency using Shannon entropy , 2011, Expert Syst. Appl..

[19]  Biao Ma,et al.  [Failure Prediction of Power-Shift Steering Transmission Based on Oil Spectral Analysis with Wiener Process]. , 2015, Guang pu xue yu guang pu fen xi = Guang pu.

[20]  Stephan Rinderknecht,et al.  Online and real-time monitoring system for remaining service life estimation of automotive transmissions – Application to a manual transmission , 2015 .

[21]  Biao Ma,et al.  A Unified System Residual Life Prediction Method Based on Selected Tribodiagnostic Data , 2019, IEEE Access.

[22]  Eric Bechhoefer,et al.  Online particle-contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines , 2015 .

[23]  David Valis,et al.  Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance , 2016, Reliab. Eng. Syst. Saf..

[24]  Y. Liu,et al.  Oil-based maintenance interval optimization for power-shift steering transmission , 2018 .

[25]  Jianjun Shi,et al.  A Data-Level Fusion Model for Developing Composite Health Indices for Degradation Modeling and Prognostic Analysis , 2013, IEEE Transactions on Automation Science and Engineering.

[26]  Biao Ma,et al.  Degradation index construction methodology for mechanical transmission based on fusion of multispectral oil data , 2019 .

[27]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[28]  Viliam Makis,et al.  Parameter estimation and remaining useful life prediction of lubricating oil with HMM , 2017 .