University of Oulu

S. Zhang, J. Zhou, E. Wang and S. Pirttikangas, "CNN4GCDD: a One-Dimensional Convolutional Neural Network-based Model for Gear Crack Depth Diagnosis," 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2022, pp. 1138-1142, doi: 10.1109/CSCWD54268.2022.9776142.

CNN4GCDD : a one-dimensional convolutional neural network-based model for gear crack depth diagnosis

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Author: Zhang, Shouhua1; Zhou, Jiehan1; Wang, Erhua2;
Organizations: 1Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
2School of Intelligent Equipment Changzhou College of Information Technology, Changzhou, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-10-10


Gear crack is one of the common failures in transmission systems. With the gradual expansion of cracks, it may cause tooth fracture. Therefore, it is of great significance to study the fault diagnosis of gear cracks. Vibration signals with time sequence are widely used in gear fault diagnosis. Extracting key fault features from vibration signals determines the accuracy of fault diagnosis models. This paper takes spur gears as research objects, and proposes a model for diagnosing gear crack depth based on one-dimensional convolutional neural network (short for CNN4GCDD). In order to identify crack depths, we collect the vibration signals from three gears with various crack depths and a normal gear without cracks. CNN4GCDD uses the original vibration signal as the input, adaptively extracts features, and makes crack depth diagnosis through the convolutional neural network. The experimental results demonstrate that CNN4GCDD can directly use the original time-domain signal for crack depth diagnosis, and make a high accurate prediction.

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ISBN: 978-1-6654-0527-0
ISBN Print: 978-1-6654-0763-2
Pages: 1138 - 1142
Article number: 9776142
DOI: 10.1109/cscwd54268.2022.9776142
Host publication: 2022 IEEE 25th international conference on computer supported cooperative work in design (CSCWD)
Conference: International Conference on Computer Supported Cooperative Work in Design
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
214 Mechanical engineering
Funding: The authors operate under Academy of Finland 6Genesis Flagship program (grants 318927) SRA4 Services and Applications.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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