计算机科学
人工智能
任务(项目管理)
滤波器(信号处理)
机器学习
模式识别(心理学)
钥匙(锁)
学习迁移
数据挖掘
断层(地质)
工程类
计算机视觉
计算机安全
地质学
地震学
系统工程
作者
Saibo Xing,Yaguo Lei,Bin Yang,Na Lu
出处
期刊:IEEE Transactions on Industrial Electronics
[Institute of Electrical and Electronics Engineers]
日期:2022-02-01
卷期号:69 (2): 1968-1976
被引量:31
标识
DOI:10.1109/tie.2021.3063975
摘要
Deep learning (DL) based diagnosis models have to be trained by large quantities of monitoring data of machines. However, in real-case scenarios, machines operate under the normal condition in most of their life time while faults seldom happen. Therefore, though massive data are accessible, most are data of the normal condition while fault data are still extremely limited. In other words, fault diagnosis of real machines is actually a few-shot diagnosis problem. To deal with few-shot diagnosis, this article proposes adaptive knowledge transfer with multiclassifier ensemble (AKTME) under the paradigm of continual machine learning. In AKTME, knowledge learned by DL models is considered to be represented by the learnable filter kernels (FKs). The key of AKTME is a proposed continual weighted updating (CWU) technique of FKs. By CWU, shared FKs are distilled from multiple auxiliary tasks and adaptively transferred to the target task. Then by multiclassifier ensemble, AKTME is able to recognize faults with few fault data accessible. AKTME is applied on two few-shot diagnosis cases. Results verify that AKTME achieves higher diagnosis accuracies than recently proposed methods. Moreover, AKTME tends to improve the diagnosis accuracy as it prelearns on more auxiliary tasks continually.
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