A Fault Diagnosis Method for Gantry Crane Trolley Reducer Bearing Under the Condition of Dataset Imbalance Based on 1DCNN-CGAN

DOI

10.2991/978-94-6463-736-6_15How to use a DOI?

Keywords

Conditionally generative adversarial networks; Deep Learning; gantry crane; Troubleshooting

Abstract

In recent years, the development of deep learning has made it more and more used to solve the problem of fault diagnosis of gantry cranes and achieved good results. However, the traditional fault diagnosis methods based on deep learning rely too much on the collective quantity and specification of data, and it is difficult to obtain the ideal generalization effect in the absence or very little of standardless data. In order to solve this problem in the case of faultless data, this paper proposes a fault diagnosis method for gantry crane trolley reducer based on deep convolution-conditioned generative adversarial network (1DCNN-CGAN). Firstly, based on the conditional generative adversarial network, this paper extracts the time-domain features of the open-source fault data and generates labeled fault data to obtain a small number of initial fault datasets. Secondly, this paper constructs a feature extractor based on the deep convolutional neural network to realize the effective noise reduction of the vibration acceleration data of the shipbuilding gantry crane upward trolley reducer. Finally, based on the working data of a gantry crane, the effectiveness of the proposed method is proved by comparing the diagnostic effects of the proposed method and the two related algorithms.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Changjie Wu
AU  - Lihan Jin
PY  - 2025
DA  - 2025/05/22
TI  - A Fault Diagnosis Method for Gantry Crane Trolley Reducer Bearing Under the Condition of Dataset Imbalance Based on 1DCNN-CGAN
BT  - Proceedings of the 2025 4th International Conference on Engineering Management and Information Science (EMIS 2025)
PB  - Atlantis Press
SP  - 113
EP  - 132
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-736-6_15
DO  - 10.2991/978-94-6463-736-6_15
ID  - Wu2025
ER  -