Building The Health Monitoring and Fault Diagnosis Models For Stamping Press

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 4, 2023
Yuan-Jen Chang Lin-Jie Chen Yuta Tu Hung-Pin Yang Chen-Kang Lee

Abstract

A stamping press is widely used for the metal forming process. To achieve continuous automation and high precision forming, monitoring the press's health and diagnosing faults during stamping is necessary. The three primary types of faults that may occur in the stamping press are lack of lubrication oil, quality variation of lubrication oil, and clearance variation, which can lead to a decline in workpiece quality and reduced lifespan of the dies and presses. This study adopted the Prognostics and Health Management (PHM) technique to implement a predictive maintenance system for the stamping press. To extract relevant data, the National Instrument (NI) DAQ was used to acquire the three-phase currents and X, Y, and Z vibration signals. Six signals provided a total of seventy-two features, and the top three key features were selected for building a health assessment model using the Logistic regression and PCA algorithms. An early warning is triggered when the health indicator drops below the threshold, alerting the operators. Additionally, fault diagnosis was achieved using classification algorithms such as Support Vector Machine (SVM), K Nearest Neighbors (K-NN), and eXtreme Gradient Boosting (XGBoost). The fault diagnosis model achieved high accuracies of up to 99%.   

Abstract 213 | PDF Downloads 190

##plugins.themes.bootstrap3.article.details##

Keywords

Stamping press, Prognostics and Health Management (PHM), Health assessment, Fault diagnosis

References
Chen, C., Zhang, Q., Yu, B., Yu, Z., Lawrence, P. J., Ma, Q., & Zhang, Y. (2020). Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier. Computers in Biology and Medicine, 123, 103899.

Chin Fong Machine Industrial Co., Ltd. (2023), acquired from Website https://chinfong.com/en/index.php

Cunningham, P., & Delany, S. J. (2021). k-Nearest neighbour classifiers-A Tutorial. ACM computing surveys (CSUR), 54(6), 1-25.

Fukunaga, K. Introduction to Statistical Pattern Recognition; Elsevier, 2013; ISBN 978-0-08-047865-4.

Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4), 18-28.

Lee, J., Ni, J., Singh, J., Jiang, B., Azamfar, M., & Feng, J. (2020). Intelligent maintenance systems and predictive manufacturing. Journal of Manufacturing Science and Engineering, 142(11).

Li, P., Jia, X., Feng, J., Davari, H., Qiao, G., Hwang, Y., & Lee, J. (2018). Prognosability study of ball screw degradation using systematic methodology. Mechanical Systems and Signal Processing, 109, 45-57.

Thieullen, A., Ouladsine, M., & Pinaton, J. (2013). Application of Principal Components Analysis to improve fault detection and diagnosis on semiconductor manufacturing equipment. In 2013 European Control Conference (ECC) (pp. 1445-1500). IEEE.
Section
Special Session Papers