In-Situ Monitoring and Prediction of Progressive Joint Wear Using Bayesian Statistics

In this paper, a statistical methodology of estimating wear coefficient and predicting wear volume in a revolute joint using in-situ measurement data is presented. An instrumented slider-crank mechanism is built, which can measure the joint force and the relative motion between the pin and bushing during operation. The former is measured using a load cell built onto a necked portion of the hollow steel pin, while the latter is measured using a capacitance probe. In order to isolate the effect of friction in other joints, a porous carbon air bearing for the revolute joint between the follower link and the slide stage, as well as a prismatic joint for the linear slide, are used. Based on the relative motion between the centers of pin and bushing, the wear volumes are estimated at six different operating cycles. The Bayesian inference technique is used to update the distribution of wear coefficient, which incorporates in-situ measurement data to obtain the posterior distribution. In order to obtain the posterior distribution, Markov Chain Monte Carlo technique is employed, which effectively draws samples of the given distribution. The results show that it is possible to narrow the distribution of wear coefficient and to predict the future wear volume with reasonable confidences. The effect of prior distribution on the wear coefficient is discussed by comparing with non-informative case.