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

Guofa Li,Zhenhua Hou,Yanbo Wang,Jialong He,Tianwei Hou,Le Du

doi:10.1155/2020/5796965

Abstract

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.

Highlights

Prognostic and health management (PHM) uses various algorithms and models to identify the mapping relationship between fault symptoms and causes, deduces the cause and location of faults, traces fault symptoms, predicts possible faults, points out the development trends and consequences of faults, predicts the residual life of components, and formulates the best maintenance and guarantee scheme. erefore, PHM technology for tool magazines is helpful for improving their reliability, safety, and adaptability
Step 6: the order of the autoregressive moving average (ARMA) model is determined on the basis of the autocorrelation function (ACF) and partial ACF (PACF) of the processed sequence. e order of the ARMA model is optimised through AIC and BIC. e unknown parameters in the model are calculated using sequence quadratic program (SQP). e ARMA model is tested via the residual test method. e failure of the tool magazine is forecasted on the basis of the established ARMA model
Reliability Test Bench and PHM System of a Tool Magazine. e reliability test bench and PHM system of a tool magazine are established as shown in Figure 4. e reliability test bench includes a bracket, a tool magazine, various sensors, a counterweight block, and a virtual spindle

Summary

Introduction

E health status of each tool magazine subcomponent is evaluated using grey clustering and an entropy weight method. 3. Fault Forecast of a Tool Magazine Based on the ARMA Model Step 2: for every subcomponent, the weight of each performance indicator is determined using an entropy weight method, and the health status of each subcomponent is evaluated using the grey clustering method.

Results

Conclusion