Integrating intrinsic information: A novel open set domain adaptation network for cross-domain fault diagnosis with multiple unknown faults

域适应 计算机科学 领域(数学分析) 断层(地质) 集合(抽象数据类型) 适应(眼睛) 数据挖掘 分布式计算 人工智能 心理学 数学 神经科学 地质学 地震学 数学分析 分类器(UML) 程序设计语言
作者
Yuteng Zhang,Hongliang Zhang,Bin Chen,Jinde Zheng,Haiyang Pan
出处
期刊:Knowledge Based Systems [Elsevier BV]
卷期号:299: 112100-112100 被引量:1
标识
DOI:10.1016/j.knosys.2024.112100
摘要

Existing popular domain adaptation approaches typically assume that the source and target domains share the same label set. However, in industrial scenarios, the equipment may encounter unknown fault modes because of the harsh operating environment, which limits the application of existing diagnostic methods. To address the above problem, an intrinsic information-guided open set domain adaptation network is proposed for cross-domain fault diagnosis with unknown faults. First, a similarity-based discrimination framework is constructed to enhance the robustness of the model for unknown samples, which learns the similarity between samples and fault prototypes to enhance the classification performance. Then, a multi-information integrated weighting module is designed to quantify the transferability of samples through enhanced domain similarity information learning and prediction uncertainty information learning methods. Additionally, a self-supervised neighborhood clustering learning method is constructed, which enables the model to learn structural information about the target domain and encourages the target samples to cluster closely for better separability. Finally, the weighted open set adversarial training framework effectively facilitates diagnostic knowledge transfer and unknown fault recognition. Comprehensive experimental results on two datasets demonstrate the effectiveness of the proposed method in addressing the open set cross-domain diagnosis problem, which achieves promising performance over the comparison methods.
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