Bayesian inference-assisted reliability analysis framework for robotic motion systems in future factories

Abstract

Reliability assessment of robotic motion systems subject to complex dynamic properties and multi-source uncertainties in open environments registers an important yet challenging task. To tackle this task, this study proposes a new reliability analysis framework for robotic motion systems, which incorporates the moment-based method and Bayesian inference-guided probabilistic model updating strategy. To start with, the fractional exponential moments calculated by the sparse grid method are adopted to quantify the uncertainty of performance indexes for robotic motion systems. Subsequently, a versatile mixture probability distribution model is established to evaluate the reliability of the performance indexes, facilitating the probability distribution modeling of various features. To capture sufficient uncertainty information of the system performance, two solution strategies for probabilistic model parameters are developed by incorporating the direct and sequential Bayesian updating methods. With fractional exponential moments, the proposed probability model is calibrated to reconstruct the probability distribution and calculate the failure probability for robotic motion systems. The effectiveness of the proposed framework is validated by three numerical examples, wherein Monte Carlo simulation and other prevailing methods are performed for comparison. The case studies indicate that the proposed framework is viable to assess the performance reliability of robotic motion systems with satisfactory computational accuracy and efficiency.