Multimodal and multi-view predictive maintenance: A case study in the oil industry

Tomas Souper, Duarte Oliper, Vitor Rolla
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Abstract

This paper explores comprehensive prognostics and health management in the oil and gas industry. It provides a proactive approach to equipment maintenance by detecting potential problems and predicting faults before they occur. Distillers are crucial oil and gas industry components, requiring regular maintenance and monitoring to maintain optimal performance and prevent unplanned maintenance. Deep learning models have shown promising results for predictive maintenance, and multimodality can bring generalization capabilities to these models. This study proposes a multimodal (and multi-view) approach for predictive maintenance in an oil and gas industry distiller dataset. The goal is to demonstrate that this approach can achieve more liable and generalizable models for predictive maintenance than state-of-the-art neural networks when training on a medium-scale dataset.
多模式和多视角预测性维护:石油行业的案例研究
本文探讨了石油和天然气行业的综合预测和健康管理。它通过检测潜在问题并在故障发生之前预测故障,为设备维护提供了一种主动的方法。蒸馏器是石油和天然气行业的关键部件,需要定期维护和监控,以保持最佳性能,防止意外维护。深度学习模型在预测性维护方面已经显示出很好的结果,而多模态可以为这些模型带来泛化能力。本研究提出了一种多模式(和多视图)的方法,用于石油和天然气工业蒸馏器数据集的预测性维护。我们的目标是证明,当在中等规模的数据集上进行训练时,这种方法可以比最先进的神经网络实现更可靠和可推广的预测性维护模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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