Development of a High-Precision Balancing Machine Algorithm Considering Dynamic Reliability Through Adaptive Dbscan

作     者：Jung, Joon Ha Kim, Byungock Na, Woong Jae Shin, Yun-Ho 

作者机构：Department of Industrial Engineerin0g Ajou University Yeongtong-gu Suwon16499 Korea Republic of Department of System Dynamics Korea Institute of Machinery & Materials Yuseong-gu Daejeon34103 Korea Republic of Department of Big Data Chungbuk National University Chungju28644 Korea Republic of Department of Naval Architecture & Ocean Engineering Pusan National University Geumjeong-gu Busan46241 Korea Republic of 

出 版 物：《SSRN》 

年 卷 期：2022年

核心收录：

主　　题：Monte Carlo methods 

摘      要：Recently, the demand for high-precision balancing of rotors has increased in the automobile industry, as more rotors are designed to rotate at ever-higher speeds to maximize energy efficiency. The accumulation of measurement uncertainty in the balancing process decreases the accuracy of the unbalanced mass estimation, which is the ultimate goal of balancing. Here, the problem of uncertainty is shown through Monte-Carlo simulation of signals acquired from an actual production line. To reduce the effect of measurement uncertainty in the balancing procedures, a signal-processing technique that increases the dynamic reliability of the signal is proposed. The suggested method is based on density-based spatial clustering of applications with noise (DBSCAN) with the use of the orthogonality-based averaging method. Specifically, by adjusting radius values, while clustering samples through the use of the DBSCAN method, the outliers that arise due to uncertainty are successfully removed. In this work, our proposed Adaptive DBSCAN method is validated by applying it to a balancing machine used for blower rotors in fuel cell electric vehicles. The results show that the estimated unbalanced mass derived by the influence coefficient method was as high as 0.71 kg-1s-2, while the proposed method reduced it to less than 0.05 kg-1s-2. In addition, the suggested procedure reduced the deviations of the unbalanced mass phase estimation by 35.2%, as compared to the results found by the conventional method. Consequently, through the validation test, the suggested method was found to have the largest vibration decrease of any method considered in the study. © 2022, The Authors. All rights reserved.