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Open Access

ARTICLE

Detection Method for Bolt Loosening of Fan Base through Bayesian Learning with Small Dataset: A Real-World Application

Zhongyun Tang1,2,3, Hanyi Xu2, Haiyang Hu1,3,*
1 School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
2 School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, 310018, China
3 Shangyu Institute of Science and Engineering, Hangzhou Dianzi University, Shaoxing, 312300, China
* Corresponding Author: Haiyang Hu. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.070616

Received 20 July 2025; Accepted 29 August 2025; Published online 02 October 2025

Abstract

With the deep integration of smart manufacturing and IoT technologies, higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection. For industrial fans, base bolt loosening faults are difficult to identify through conventional spectrum analysis, and the extreme scarcity of fault data leads to limited training datasets, making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity. This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution. This method proposes specific data processing approaches and a configuration of Bayesian Convolutional Neural Network (BCNN). It can effectively improve the model’s generalization ability. Experimental results demonstrate high detection accuracy and alignment with real-world applications, offering practical significance and reference value for industrial fan bolt loosening detection under data-limited conditions.

Keywords

Bolt loosening detection; industrial small dataset; Bayesian learning; interpretability; real-world application

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