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On the Effect of Loss Function in GAN Based Data Augmentation for Fault Diagnosis of an Industrial Robot

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Applications of Generative AI

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Abstract

Intelligent fault diagnosis often requires a balanced dataset which is hard to be obtained in industrial equipments, often resulting in an imbalance between data in normal and data in the presence of faults. Data augmentation techniques are among the most promising approaches to mitigate such issue. Generative adversarial networks (GAN) are a type of generative model consisting of a generator module and a discriminator. Through adversarial learning between these modules, the optimised generator can produce synthetic patterns that can be used for data augmentation. We investigate the role of loss function in improving the training efficiency of GAN. We proposed a generalization of both mean square error (MSE GAN) and Wasserstein GAN with gradient penalty (WGAN-GP), referred to as VGAN (the V-matrix based GAN) to mitigate training instability. Also, we investigate the sliced Wasserstein distance (SWD) as the loss function of a cycle consistency generative adversarial network (CycleGAN), referred to as SW-CycleGAN. Both two models are evaluated on an industrial robot data set. Experimental results show that the proposed loss functions outperform other competitive models especially in terms of computational costs.

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Acknowledgements

This work is supported in part by Portuguese funds through FCT-Foundation for Science and Technology, in part by I.P., through IDMEC, under LAETA Project UIDB/50022/2020, in part by the National Natural Science Foundation of China under Grant 52175080, in part by the Chongqing Natural Science Foundation under Grant cstc2019jcyj-zdxmX0013, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019B1515120095, and in part by the Intelligent Manufacturing PHM Innovation Team Program under Grant 2018KCXTD029.

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Authors and Affiliations

  1. School of Mechanical Engineering, Chongqing Technology and Business University, 400067, Chongqing, China

    Ziqiang Pu & Chuan Li

  2. Universidade do Algarve, Faro, Portugal

    José Valente de Oliveira

  3. Center of Intelligent Systems, IDMEC/LAETA, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    José Valente de Oliveira

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  1. Ziqiang Pu
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Correspondence to Ziqiang Pu .

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  1. Department of Game Design, Uppsala Universitet, Visby, Sweden

    Zhihan Lyu

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Pu, Z., Li, C., de Oliveira, J.V. (2024). On the Effect of Loss Function in GAN Based Data Augmentation for Fault Diagnosis of an Industrial Robot. In: Lyu, Z. (eds) Applications of Generative AI. Springer, Cham. https://doi.org/10.1007/978-3-031-46238-2_16

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  • DOI: https://doi.org/10.1007/978-3-031-46238-2_16

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