Building The Health Monitoring and Fault Diagnosis Models For Stamping Press

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%.