A new approach for product reliability prediction by considering the production factory lifecycle information

Product reliability prediction is essential for OEMs to plan product maintenance and design improvement. Traditional approaches rely on 'product' lifecycle data for reliability prediction, often not capturing the uncertainties in OEMs' decision-making. To address this, the present work focuses on 'factory' lifecycle information in reliability prediction by introducing the concept of 'factory age,' i.e., cumulative interval for the observed factory lifecycle. The failure times for each product, in each factory age interval, were used to estimate the Weibull parameters, creating temporal data. A combination of grey- and support vector machine (SVM)-models, which complement each other in capturing global and local trends, and handling uncertainty from limited temporal data, was proposed to forecast the Weibull parameters accurately in the future factory age interval. The proposed approach was validated on two failure modes in a factory-producing turning centers, using data from the first 11 factory age intervals for model development. Reliability predictions for the last three intervals achieved root mean square errors (RMSEs) of 0.67 % and 1.48 % for failure modes I and II. Comparatively, individual grey (4.37 %, 5.11 %) and SVM (8.03 %, 10.60 %) models yielded higher RMSEs, while other reported models in literature showed in the range of 1.63 %-34.07 %, demonstrating the proposed approach's efficacy.