学习迁移
人工智能
计算机科学
深度学习
机器学习
无监督学习
概括性
工作量
故障检测与隔离
数据建模
模式识别(心理学)
心理学
数据库
执行机构
心理治疗师
操作系统
作者
Jinglun Liang,Qin Liang,Zhaoqian Wu,Haolun Chen,Shaohui Zhang,Fei Jiang
标识
DOI:10.1109/tii.2023.3258966
摘要
Recent diagnostic approaches based on the deep learning model have attracted much attention. However, developing an outstanding AI diagnostic model requires many training samples with labeled information. Moreover, training deep models is labor-intensive and time-consuming, and labeling samples and training models increase workload. To overcome these problems, this article proposes an unsupervised deep transfer learning (DTL) method with an isolation forest (iForest) for machine fault diagnosis. First, the isolation forest is used to classify and label the samples automatically; then, these labeled data are used to train deep learning (DL) models; finally, small data with the label of the target domain are used to fine-tune parameters and complete the fault diagnosis. The proposed approach has been validated with the fan gearbox dataset, the bearing dataset, and the ball screw dataset. The results show that the proposed unsupervised deep transfer learning model has high accuracy and generality.
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