加权
断层(地质)
计算机科学
规范(哲学)
模式识别(心理学)
领域(数学分析)
人工智能
集合(抽象数据类型)
数据挖掘
特征(语言学)
算法
数学
哲学
法学
程序设计语言
地震学
数学分析
地质学
放射科
医学
语言学
政治学
作者
Feng Jia,Yuanfei Wang,Jianjun Shen,Lifei Hao,Zhaoyu Jiang
标识
DOI:10.1088/1361-6501/ad282f
摘要
Abstract Cross-domain fault diagnosis of bearings has attracted significant attention. However, traditional cross-domain diagnostic methods have the following shortcomings: (1) when the trained model is applied to a new scenario, it leads to severe degradation of the model and a reduction in its generalisation ability. (2) The accuracy of the open-set fault diagnosis is affected by additional faults in the target domain data. To overcome these shortcomings, a stepwise feature norm network with adaptive weighting (SFNAW) is proposed for cross-domain open-set fault diagnosis. In SFNAW, two weight extractors are designed to adaptively calculate the sample weights such that a threshold can be set to mark the additional fault samples of the target domain as unknown faults using these weights. Transferable features are obtained by adaptively increasing the feature norm stepwise to alleviate model degradation and align the source and target domains. Finally, the fault diagnosis knowledge of the source domain is transferred to fault recognition in the target domain. The proposed SFNAW method was verified using two bearing datasets. The results show that the SFNAW can effectively detect additional faults in the target domain and reduce model degradation, thereby improving the fault diagnosis accuracy. Meanwhile, the SFNAW method has a higher accuracy than other traditional methods.
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