Hyperparameter Tuning and Feature Selection for Improving Flow Instability Detection in Offshore Oil Wells

Carvalho, Bruno Guilherme; Vargas, Ricardo Emanuel Vaz; Salgado, Ricardo Menezes; Munaro, Celso J.; Varejão, Flávio Miguel

doi:10.1109/indin45523.2021.9557415

Cited by 3 publications

(1 citation statement)

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“…The comparison shows a predominance of tree‐based models, in accordance with the proposal of Marins et al (2021). Finally, Carvalho et al (2021) compares many pre‐processing techniques and hyperparameters optimization strategies, showing the ineffectiveness of grid search in performing hyperparameters optimization. Furthermore, the work in its conclusion mentions the possibility of applying a DL approach in this dataset.…”

Section: Related Workmentioning

confidence: 99%

Predictive maintenance for offshore oil wells by means of deep learning features extraction

et al. 2022

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Nowadays, the great diffusion of the Internet of Things and the improvements in Artificial Intelligence techniques have given a rise in the development and application of data‐driven approaches for Predictive Maintenance to reduce the costs linked to the maintenance of industrial machinery. Due to the wide real‐life applications and the strong interest by even more industries, this field is highly attractive for academics and practitioners. So, constructing efficient frameworks to address the Predictive Maintenance problem is an open debate. In this work, we propose a Deep Learning approach for the feature extraction in the offshore oil wells monitoring context, exploiting the public 3 W dataset, which is well‐known in the literature. The dataset is made up of about 2000 multivariate time series labelled according to the corresponding functioning of the well. So, there is a classification task with eight classes, each related to a particular machinery condition. Thanks to the peculiarities of the labels, the proposed framework is valid both for diagnostics and prognostics. In more detail, we compare two different approaches in feature extraction. The first is a statistical approach, widely used in the literature related to the considered dataset; the second is based on Convolutional 1D AutoEncoder. The extracted features are then used as input for several Machine Learning algorithms, namely the Random Forest, Nearest Neighbours, Gaussian Naive Bayes and Quadratic Discriminant Analysis. Different experiments on various time horizons prove the worthiness of the Convolutional AutoEncoder.

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Section: Related Workmentioning

confidence: 99%