# Probabilistic Bearing Fault Diagnosis Using Gaussian Process with Tailored Feature Extraction

arXiv: https://arxiv.org/abs/2109.09189 · HTML: https://mlsanity.com/paper/2109.09189/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 19 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning](https://mlsanity.com/paper/1710.08904/) (2017)
- [Stacked Autoencoder Based Deep Random Vector Functional Link Neural Network for Classification](https://mlsanity.com/paper/1910.01858/) (2019)
- [Uncertainty Quantification of Mode Shape Variation Utilizing Multi-Level Multi-Response Gaussian Process](https://mlsanity.com/paper/2002.09287/) (2020)
- [Structural Model Updating Using Adaptive Multi-Response Gaussian Process Meta-modeling](https://mlsanity.com/paper/2004.11698/) (2020)

## Abstract (arXiv)

Rolling bearings are subject to various faults due to its long-time operation under harsh environment, which will lead to unexpected breakdown of machinery system and cause severe accidents. Deep learning methods recently have gained growing interests and extensively applied in the data-driven bearing fault diagnosis. However, current deep learning methods perform the bearing fault diagnosis in the form of deterministic classification, which overlook the uncertainties that inevitably exist in actual practice. To tackle this issue, in this research we develop a probabilistic fault diagnosis framework that can account for the uncertainty effect in prediction, which bears practical significance. This framework fully leverages the probabilistic feature of Gaussian process classifier (GPC). To facilitate the establishment of high-fidelity GPC, the tailored feature extraction with dimensionality reduction method can be optimally determined through the cross validation-based grid search upon a prespecified method pool consisting of various kernel principal component analysis (KPCA) methods and stacked autoencoder. This strategy can ensure the complex nonlinear relations between the features and faults to be adequately characterized. Furthermore, the sensor fusion concept is adopted to enhance the diagnosis performance. As compared with the traditional deep learning methods, this proposed framework usually requires less labeled data and less effort for parameter tuning. Systematic case studies using the publicly accessible experimental rolling bearing dataset are carried out to validate this new framework. Various influencing factors on fault diagnosis performance also are thoroughly investigated.