A Novel Unsupervised Deep Transfer Learning Method With Isolation Forest for Machine Fault Diagnosis
Jinglun Liang, Qin Liang, Zhaoqian Wu, Haolun Chen, Shaohui Zhang, Fei Jiang
Abstract
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.