Fault Diagnosis Method of Forging Press Based on Improved CNN

Abstract

The forging press plays a crucial and indispensable role in industries such as metallurgy, automotive, and aerospace. Its stable and efficient operation directly impacts the productivity and product quality of the entire production line. Therefore, it is particularly important to carry out efficient fault diagnosis for forging presses. In this paper, a method combining Convolutional Neural Network (CNN), Principal Component Analysis (PCA) and Transmission Support Vector Machine (TSVM) is proposed, which is called Multi-modal Attention-based CNN with PCA and TSVM (MACNN-PTSVM). This method effectively integrates multi-sensor information fusion technology, attention mechanism, CNN, PCA technology and TSVM, fully utilizing data collected from various sensors of the forging press. The constructed network model takes smooth monitoring signal data from the die forging press as input samples. Fault features are extracted through CNN, and PCA is utilized for feature fusion. Applications of this model in forging press fault diagnosis demonstrate significant advantages in fault classification performance. Specifically, its semi-supervised learning technology exhibits higher generalization ability and diagnostic accuracy when dealing with unlabeled data.

Keywords

• Forging press
• fault diagnosis
• CNN
• TSVM