Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

A deep learning framework for intelligent fault diagnosis using AutoML-CNN and image-like data fusion

Gao, Yan ORCID:, Chai, Chengzhang, Li, Haijiang ORCID: and Fu, Weiqi 2023. A deep learning framework for intelligent fault diagnosis using AutoML-CNN and image-like data fusion. Machines 10.3390/machines11100932

[thumbnail of machines-11-00932.pdf]
PDF - Published Version
Available under License Creative Commons Attribution.

Download (5MB) | Preview


Intelligent fault diagnosis (IFD) is essential for preventative maintenance (PM) in Industry 4.0. Data-driven approaches have been widely accepted for IFD in smart manufacturing, and various deep learning (DL) models have been developed for different datasets and scenarios. However, an automatic and unified DL framework for developing IFD applications is still required. Hence, this work proposes an efficient framework integrating popular convolutional neural networks (CNNs) for IFD based on time-series data by leveraging automated machine learning (AutoML) and image-like data fusion. After normalisation, uniaxial or triaxial signals are reconstructed into -channel pseudo-images to satisfy the input requirements for CNNs and achieve data-level fusion simultaneously. Then, the model training, hyperparameter optimisation, and evaluation can be taken automatically based on AutoML. Finally, the selected model can be deployed on a cloud server or an edge device (via tiny machine learning). The proposed framework and method were validated via two case studies, demonstrating the framework’s availability for the automatic development of IFD applications and the effectiveness of the proposed data-level fusion method.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: MDPI
ISSN: 2075-1702
Date of First Compliant Deposit: 28 September 2023
Date of Acceptance: 26 September 2023
Last Modified: 03 Oct 2023 18:35

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics