Fault Forecasting of a Machining Center Tool Magazine Based on Health Assessment

• Main Authors: Guofa Li, Yanbo Wang, Jialong He, Tianwei Hou, Le Du, Zhenhua Hou
• Format: Article
• Language: English
• Published: Hindawi Limited 2020-01-01
• Series: Mathematical Problems in Engineering
• Online Access: http://dx.doi.org/10.1155/2020/5796965

A tool magazine is one of the key functional components of machining centers with frequent faults. The reliability level of a tool magazine directly affects the reliability level of the machining center. After establishing a reliability test bench and a prognostic and health management system for a tool magazine, a novel fault-forecasting method for machining center tool magazines based on health assessment is proposed. First, the health status of each tool magazine subcomponent is determined using the grey clustering method. Second, the weight of each tool magazine subcomponent is determined using an entropy weight method. Third, the health status of the tool magazine is evaluated via fuzzy comprehensive evaluation. If the tool magazine exhibits an unhealthy status, then the subcomponent with the worst health status is selected for fault forecasting. In addition, standardized treatment, stationarity test, and differential processing are conducted separately on the raw performance indicator data of the worst subcomponent. Finally, the performance indicators of the worst subcomponent are forecasted with the constructed autoregressive moving average model. Using tool-falling failure as an example, the forecasted and experimental tool-pulling forces are compared and analyzed, and the prediction accuracy of the proposed method is verified.