A multi-stage semi-supervised improved deep embedded clustering (MS-SSIDEC) method for bearing fault diagnosis under the situation of insufficient labeled samples

• URL: http://arxiv.org/abs/2109.13521v1
• Date: Tue, 28 Sep 2021 06:49:40 GMT
• Title: A multi-stage semi-supervised improved deep embedded clustering (MS-SSIDEC) method for bearing fault diagnosis under the situation of insufficient labeled samples
• Authors: Tongda Sun, Gang Yu
• Abstract summary: It costs a lot of labor and time to label data in actual industrial processes, which challenges the application of intelligent fault diagnosis methods. To solve this problem, a multi-stage semi-supervised improved deep embedded clustering (MS-SSIDEC) method is proposed. This method includes three stages: pre-training, deep clustering and enhanced supervised learning.
• Score: 20.952315351460527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent data-driven fault diagnosis methods have been widely applied, but most of these methods need a large number of high-quality labeled samples. It costs a lot of labor and time to label data in actual industrial processes, which challenges the application of intelligent fault diagnosis methods. To solve this problem, a multi-stage semi-supervised improved deep embedded clustering (MS-SSIDEC) method is proposed for the bearing fault diagnosis under the insufficient labeled samples situation. This method includes three stages: pre-training, deep clustering and enhanced supervised learning. In the first stage, a skip-connection based convolutional auto-encoder (SCCAE) is proposed and pre-trained to automatically learn low-dimensional representations. In the second stage, a semi-supervised improved deep embedded clustering (SSIDEC) model that integrates the pre-trained auto-encoder with a clustering layer is proposed for deep clustering. Additionally, virtual adversarial training (VAT) is introduced as a regularization term to overcome the overfitting in the model's training. In the third stage, high-quality clustering results obtained in the second stage are assigned to unlabeled samples as pseudo labels. The labeled dataset is augmented by those pseudo-labeled samples and used to train a bearing fault discriminative model. The effectiveness of the method is evaluated on the Case Western Reserve University (CWRU) bearing dataset. The results show that the method can not only satisfy the semi-supervised learning under a small number of labeled samples, but also solve the problem of unsupervised learning, and has achieved better results than traditional diagnosis methods. This method provides a new research idea for fault diagnosis with limited labeled samples by effectively using unsupervised data.

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