The Optimization of Maintenance Policy by Using Generative Adversarial Networks for Data Augmentation under Limited Information
Pages 165 - 166
Abstract
The datasets play a crucial role in accurately assessing the condition of equipment for the successful implementation of Predictive Maintenance (PdM). However, in practice, challenges related to the acquisition and application of equipment data must be addressed as a priority. Sometimes, the paucity of condition monitoring data pertinent to specific equipment presents a formidable impediment to the formulation of precise maintenance predictions. This predicament predominantly arises from the exorbitant costs and constraints associated with the monitoring milieu.
In this article, we propose a method by using generative adversarial networks (GANs) for data augmentation under the limited information situation. Utilizing the estimated Weibull distribution from generated data, we predict equipment failure probabilities. Leveraging these probabilities, successful implementation of Predictive Maintenance (PdM) is achieved. Moreover, with an iterative and enhanced precinct in data generated by GANs, the predictions of remaining lifespan align more closely with real-world scenarios. This empowers managers to timely fine-tune maintenance strategies, thereby mitigating costs.
References
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Ian J. Goodfellow, Jean Pouget-Abadie, and Mehdi Mirza. 2014. Generative adversarial nets. In Proceedings of the 2014 Conference on Advances in Neural Information Processing Systems 3, Vol. 27. 2672–2680.
[2]
Jihyun Lee and Sunneung Ahn. 2018. Age replacement model using the parameter estimation of Weibull distribution with censored lifetimes. In IEEE International Conference on Prognostics and Health Management (ICPHM).
[3]
Ziqiang Pu, Diego Cabrera, and Chuan Li. 2022. A one-class generative adversarial detection framework for multifunctional fault diagnoses. IEEE Transactions on Industrial Electronics 69, 8 (2022), 8411–8419.
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Jianhua Zhao, Qiang Fu, Huawei Wang, and Xiaojing Yan. 2020. A maintenance-prediction method for aircraft engines using generative adversarial networks. In IEEE International Conference on Computer and Communications.
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Zhibin Zhao, Chuang Sun, Ruqiang Yan, Jingyao Wu, and Xuefeng Chen. 2020. Fault-attention generative probabilistic adversarial autoencoder for machine anomaly detection. IEEE Transactions on Industrial Informatics 16 (2020).
Index Terms
- The Optimization of Maintenance Policy by Using Generative Adversarial Networks for Data Augmentation under Limited Information
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Published In
December 2023
175 pages
ISBN:9798400709050
DOI:10.1145/3659154
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Association for Computing Machinery
New York, NY, United States
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Published: 26 December 2024
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ICEA 2023
ICEA 2023: The 2023 International Conference on Intelligent Computing and Its Emerging Applications
December 14 - 16, 2023
Kaohsiung, Taiwan
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