Cross-Modal Fusion Convolutional Neural Networks With Online Soft-Label Training Strategy for Mechanical Fault Diagnosis

过度拟合 计算机科学 卷积神经网络 人工智能 断层(地质) 特征(语言学) 特征学习 情态动词 模式识别(心理学) 机器学习 特征提取 过程(计算) 深度学习 人工神经网络 数据挖掘 语言学 哲学 化学 地震学 高分子化学 地质学 操作系统
作者
Yadong Xu,Ke Feng,Xiaoan Yan,Xin Sheng,Beibei Sun,Zheng Liu,Ruqiang Yan
出处
期刊:IEEE Transactions on Industrial Informatics [Institute of Electrical and Electronics Engineers]
卷期号:20 (1): 73-84 被引量:29
标识
DOI:10.1109/tii.2023.3256400
摘要

CNN-based fault detection approaches based on multisource signals have attracted increasing interest from the research community and industrial practices, thanks to the powerful feature representation capability of CNN and the rapid development of sensor technology. Various strategies have been applied in existing CNN-based diagnostic models to learn features from 1D real-valued multivariate data. However, the distribution gap and the intrinsic correlations among multisource mechanical signals during the learning process have been rarely considered, which may lead to suboptimal fault identification results. To tackle this issue, this paper proposes a cross-modal fusion convolutional neural network (CMFCNN) for mechanical fault diagnosis, which performs modality-specific and cross-modal feature representation on multisource data. Specifically, CMFCNN adopts two parallel modality-specific networks and a cross-modal knowledge-sharing network to fully explore independent and shared features from the multisource mechanical signals. To achieve effective feature propagation and fusion, a cross-modal fusion module (CMF) is introduced to integrate cross-modal features and pass the fused information to the next layer. Moreover, to alleviate overfitting and achieve a better diagnostic performance of the framework, an online soft label training (OSLT) algorithm is adopted in the CMFCNN training phase. Extensive experimental results on the cylindrical rolling bearing dataset and the planetary gearbox dataset validate that the proposed CMFCNN outperforms seven state-of-the-art methods significantly, especially under strong noise conditions.
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