Residual life prediction of wet clutch based on binary Wiener process

Abstract

PurposeIn armored vehicles integrated transmissions, residual life prediction based on oil spectrum data is crucial for condition monitoring and reliability assessment. This paper aims to use the advantages of real-time and accurate prediction of binary Wiener process, the residual life prediction of clutch is studied.Design/methodology/approachFirst, combined with the wet clutch life test, the indicator elements Cu and Pb and the failure threshold of the residual life prediction of the clutch are extracted through the oil replacement correction of the spectral data of the whole life cycle; second, the correlation characteristics of indicating elements are analyzed by MATLAB Copula function, then the correlation function of residual life will be derived; third, according to the inverse Gaussian principle, the performance degradation mathematical models of the unary and binary Wiener processes of the above two indicator elements are established; finally, the maximum likelihood estimation method is used to estimate the parameters, and the monadic and binary performance degradation mathematical models are used to predict the residual life of the tested clutch.FindingsBy comparing the prediction results with the test results, with the passage of time, 81.25% of the predicted value error of the residual life prediction method based on the binary Wiener process is controlled within 20%, while 56.25% of the predicted value error of the residual life prediction method based on the unitary Wiener process is controlled within 20%. At the same time, the prediction accuracy of the binary prediction model is 2%–16.7% higher than that of the unitary prediction model.Originality/valueThis paper studies the residual life prediction theory of wet clutch, which can develop the theory and method of comprehensive transmission health monitoring, and provide theoretical and technical support for the construction of a reliable health management system for high-speed tracked vehicles.