判别式
马氏距离
计算机科学
人工智能
特征(语言学)
机器学习
仿射变换
公制(单位)
断层(地质)
模式识别(心理学)
特征工程
钥匙(锁)
领域(数学分析)
元学习(计算机科学)
特征向量
特征学习
深度学习
数据挖掘
数学
工程类
任务(项目管理)
数学分析
哲学
语言学
运营管理
计算机安全
地震学
纯数学
地质学
系统工程
作者
Jianyu Long,Rongxin Zhang,Yibin Chen,Rongguo Zhao,Zhe Yang,Y. Y. Huang,Chuan Li
出处
期刊:IEEE-ASME Transactions on Mechatronics
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-11
标识
DOI:10.1109/tmech.2023.3300359
摘要
Few-shot fault diagnosis aims to detect novel faults with only a few labeled samples in each category. Most of the few-shot learning (FSL)–based fault diagnosis models use meta-learning frameworks because of their effectiveness and simplicity. However, these models often fail to be generalized in unseen working conditions that exhibit domain shifts. This study focuses on the few-shot fault diagnosis while addressing the challenges in domain-shift scenarios by developing a customized meta-learning framework, which consists of three key contributions: 1) a fused deep feature learning strategy is designed using multidomain signals in time, frequency, and time–frequency to extract more discriminative features from a few labeled samples; 2) a domain shift–learned feature transformation layer is introduced by modulating the feature activations with affine transformations into the meta-learner to tackle challenges due to domain shifts under unseen working conditions; and 3) a Mahalanobis distance–based metric function is constructed leveraging an additional neural network to learn the spread variance of each fault pattern to ensure an accurate and robust label prediction. The proposed framework is tested using real-world datasets and the ablation study demonstrates the effectiveness of its key components. The results also show that the proposed framework outperforms the state-of-the-art FSL algorithms that fail to consider the domain-shift scenarios.
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