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

arXiv: https://arxiv.org/abs/2109.13521 · HTML: https://mlsanity.com/paper/2109.13521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 65 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [ClusterGAN : Latent Space Clustering in Generative Adversarial Networks](https://mlsanity.com/paper/1809.03627/) (2018)
- [Clustering with Deep Learning: Taxonomy and New Methods](https://mlsanity.com/paper/1801.07648/) (2018)
- [Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features](https://mlsanity.com/paper/1802.01059/) (2018)
- [RDEC: Integrating Regularization into Deep Embedded Clustering for Imbalanced Datasets](https://mlsanity.com/paper/1812.02293/) (2018)
- [Lateral Connections in Denoising Autoencoders Support Supervised Learning](https://mlsanity.com/paper/1504.08215/) (2015)

## Abstract (arXiv)

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.