A novel TCN-GRU based open set method for unknown damage diagnosis

Abstract

Abstract In the aerospace and high-speed rail industries, CFRP has seen widespread application. CFRP plates and connectors in operation are often subjected to impacts that can cause damage. The unpredictable nature of the impacts introduces uncertainties in both the location and extent of the damage, posing significant challenges to traditional supervised learning models, which often struggle with missed detections or misclassifications when identifying unknown damages. To address the issue, a deep learning model based on TCN-GRU is proposed. TCN extracts features from the raw time domain signals, and GRU selectively retains the significant features and completes sequence modeling. A center loss function is incorporated into the fully connected layer to improve the effects of intra-class aggregation and inter-class separation. An unknown detection module is introduced to realize the identification and classification of unknown damages based on a predefined threshold. The experimental results indicate that the proposed method can achieve effective unknown damage diagnosis in the open set case. This study provides a feasible solution for open set unknown damage diagnosis in CFRP plates and connectors.