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 that can measure the joint force and the relative motion between the pin and bushing is built. 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 the 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 coefficients, which incorporates in situ measurement data to obtain the posterior distribution. The Markov Chain Monte Carlo technique is employed to generate samples from the given distribution. The results show that it is possible to narrow the distribution of wear coefficients and to predict the future wear volume with reasonable confidence. The effect of the prior distribution on the wear coefficient is discussed by comparing with the non-informative case.