Affiliations: [a] Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, Changchun, Jilin, China
| [b] FAW Jiefang Group Co., Ltd, Changchun, Jilin, China
Correspondence:
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Corresponding authors. Jialong He, Associate Professor, School of Mechanical and Aerospace Engineering, Jilin University, Changchun, Jinlin, 130022, China. E-mail: [email protected] and Jili Wang, School of Mechanical and Aerospace Engineering, Jilin University, Changchun, Jinlin, 130022, China. E-mail: [email protected].
Abstract: The reliability of machine tool components, particularly the tool magazine manipulator, significantly affects the overall performance of the machine tool. To address the challenge of accurately evaluating the manipulator’s health status using a single performance indicator, this study proposes a method that combines Fuzzy Comprehensive Evaluation (FCE) and a Combined Weighting Method (CWM). By considering both subjective and objective factors, this method provides a comprehensive evaluation of the manipulator’s health status, enhancing the accuracy and reliability of the assessment. The method utilizes fuzzy distribution to construct membership matrices for different health levels and adopts the CWM that combines the Entropy Weight Method (EWM) and Analytic Hierarchy Process (AHP) to determine the combined weights of the health evaluation indices. This approach improves the accuracy and reliability by considering multiple indicators and objectively weighting them based on their importance. The current health status of the manipulator is evaluated using the fuzzy weighted average operator and the maximum membership principle. Moreover, a fault prediction method based on Particle Swarm Optimization (PSO) and GM(1,1) is proposed to overcome the information gap and small sample problems. The proposed model’s prediction accuracy is verified by comparing it with other models, demonstrating its effectiveness and reliability.
Keywords: Health status evaluation, fault prediction, fuzzy comprehensive evaluation, grey model, particle swarm optimization
