Transfer Learning for Bearing Fault Diagnosis based on Graph Neural Network with Dilated KNN and Adversarial Discriminative Domain Adaptation

判别式 域适应 计算机科学 学习迁移 对抗制 人工智能 人工神经网络 适应(眼睛) 模式识别(心理学) 断层(地质) 领域(数学分析) 方位(导航) 图形 机器学习 理论计算机科学 数学 心理学 地质学 神经科学 地震学 分类器(UML) 数学分析
作者
Tang Tang,Zeyuan Liu,Chuanhang Qiu,Ming Chen,Ying Yu
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:35 (6): 065106-065106 被引量:3
标识
DOI:10.1088/1361-6501/ad3016
摘要

Abstract Graph neural networks (GNNs) have emerged as a forefront in deep learning, notably influencing research in mechanical fault diagnosis. Transfer learning, particularly through domain adaptation (DA) techniques, has found application in machinery fault diagnosis by training models under one working condition and deploying them under another. While efforts have been made to integrate GNNs with DA techniques to alleviate data distribution discrepancies by investigating the inter-sample relationships, challenges persist: reliance on K -nearest neighbor (KNN) for graph generation emphasizes close relationships, neglecting distant ones; batch processing limits real-time fault diagnosis; and transfer between different-sized bearings is nearly unexplored. To address these limitations, a novel framework for GNN-based domain adaptation in machinery fault diagnosis is proposed. Initially, a convolutional neural network extracts node embeddings from the continuous wavelet transform graph of raw vibration signals. Subsequently, a graph generation layer based on dilated KNN captures both close and distant sample relationships, addressing the long-range dependency issue. Two GNN blocks are then applied for inter-sample relationships investigation and further feature extraction with the outputs directed to a linear classifier during source domain pretraining. Following pretraining, adversarial discriminative domain adaptation is leveraged to mitigate domain distribution discrepancies. Additionally, a novel graph construction method that combines existing training samples with a new single sample is proposed, enabling fault prediction with single instances for real-time online fault diagnosis. Evaluation on datasets with varying working conditions and bearings of different sizes demonstrates the superior performance of our method to other comparison methods.
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