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

TL;DR

This paper introduces a multi-stage semi-supervised deep clustering method that effectively diagnoses bearing faults with limited labeled data by leveraging unlabeled data through a combination of auto-encoding, clustering, and pseudo-labeling techniques.

Contribution

It proposes a novel multi-stage framework combining semi-supervised learning, improved deep embedded clustering, and adversarial training to enhance fault diagnosis with scarce labeled samples.

Findings

Achieves high accuracy in semi-supervised fault diagnosis.

Effectively utilizes unlabeled data to improve clustering.

Demonstrates robustness on public bearing datasets.

Abstract

Although data-driven fault diagnosis methods have been widely applied, massive labeled data are required for model training. However, a difficulty of implementing this in real industries hinders the application of these methods. Hence, an effective diagnostic approach that can work well in such situation is urgently needed.In this study, a multi-stage semi-supervised improved deep embedded clustering (MS-SSIDEC) method, which combines semi-supervised learning with improved deep embedded clustering (IDEC), is proposed to jointly explore scarce labeled data and massive unlabeled data. In the first stage, a skip-connection-based convolutional auto-encoder (SCCAE) that can automatically map the unlabeled data into a low-dimensional feature space is proposed and pre-trained to be a fault feature extractor. In the second stage, a semi-supervised improved deep embedded clustering (SSIDEC) network is proposed for clustering. It is first initialized with available labeled data and then used to simultaneously optimize the clustering label assignment and make the feature space to be more clustering-friendly. To tackle the phenomenon of overfitting, virtual adversarial training (VAT) is introduced as a regularization term in this stage. In the third stage, pseudo labels are obtained by the high-quality results of SSIDEC. The labeled dataset can be augmented by these pseudo-labeled data and then leveraged to train a bearing fault diagnosis model. Two public datasets of vibration data from rolling bearings are used to evaluate the performance of the proposed method. Experimental results indicate that the proposed method achieves a promising performance in both semi-supervised and unsupervised fault diagnosis tasks. This method provides a new approach for fault diagnosis under the situation of limited labeled samples by effectively exploring unsupervised data.