Identification of weak buses for proper placement of reactive compensation through sensitivity analysis using a neural network surrogate model

Guevara, Isaac; Gutierrez, Marco; Zuniga, Pavel

doi:10.1109/ropec.2015.7395084

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Surrogatebased methods provide an analytic approach to construct mathematical model or prediction model from those sensor data. The widely used surrogate models, such as Kriging [13], Gaussian surrogate model [14], the Radial Basis Function surrogate models [15] and ANN surrogate model [16] can be effectively used for practical SA. ANN plays the dual role in our project for both prediction model and surrogate model of SA.…”

Section: Simplification Of Ann Model Via Sensitivity Analysis System mentioning

confidence: 99%

Simplifying Neural Network Based Model for Ship Motion Prediction: A Comparative Study of Sensitivity Analysis

Cheng

Chen

Diao

et al. 2017

Volume 1: Offshore Technology

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This paper presents a comparative study of sensitivity analysis (SA) and simplification on artificial neural network (ANN) based model used for ship motion prediction. Considering traditional structural complexity of ANN usually results in slow convergence, SA, as an efficient tool for correlation analysis, can help to reconstruct the ANN model for ship motion prediction. An ANN-Garson method and an ANN-EFAST method are proposed, both of which utilize the ANN for modeling but select the input parameters in a local and a global fashion, respectively. Through the benchmark tests, ANN-EFAST exhibits superior performance in both linear and nonlinear systems. Further test on ANN-EFAST via a case study of ship heading prediction shows its cost-effective and timely in compacting the ANN based prediction model.

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Section: Simplification Of Ann Model Via Sensitivity Analysis System mentioning

confidence: 99%

Simplifying Neural Network Based Model for Ship Motion Prediction: A Comparative Study of Sensitivity Analysis

Cheng

Chen

Diao

et al. 2017

Volume 1: Offshore Technology

View full text Add to dashboard Cite

show abstract

“…The surrogate model can learn from the historical sensor data to find the inherent relationship between model output and input. The widely used surrogate models including polynomials, splines, Gaussian Processing (GP) [10], SVM and Artificial Neural Network (ANN) [11], have been effectively used for practical SA. However, polynomials, spline, and GP are not always efficient, especially in the case of complex nonlinear phenomena.…”

Section: Introductionmentioning

confidence: 99%

A SVM-based Sensitivity Analysis Approach for Data-Driven Modeling of Ship Motion

Wang

Cheng

Chen

et al. 2018

2018 IEEE International Conference on Mechatronics and Automation (ICMA)

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This paper presents a novel method that combines support vector machine (SVM) with sensitivity analysis (SA) to analyze sensor data for ship motion modeling. In order to investigate how each model input contributes to the model's output, the PAWN method which is based on cumulative distribution function (CDF) is used. Considering the limitation of the PAWN method that it cannot be applied to sensor data directly, a surrogate model using SVM is integrated into PAWN method as it has a better solution of non-linearity and high-dimension problem as well as excellent generalization capability. Implementation of whole systematic workflow is elaborated in detail and related experiments are made such as comparison of four distance metric methods, benchmark test and SA of vessel data. The results show the proposed method can be used for analyzing vessel data to seek prime parameters that affect ship motion.

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Application of artificial intelligence to predict rock strength and drilling efficiency using in-cutter sensing data and vibration modes

Koulidis,

Ooi,

Ahmed

2024

J Petrol Explor Prod Technol

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Drilling is a complex destructive action that induces vibrations due to the rock-bit interaction, which affects the overall drilling efficiency and wellbore quality. This study aims to enhance drilling efficiency by deploying artificial neural networks (ANNs) to integrate in-cutter force sensing and vibration data. Data is collected from experiments conducted with sharp cutters on rock samples of varying mechanical properties, measuring variables such as weight on bit, torque, rotational speed, in-cutter force, and vibration measurements. A scoring system is used to evaluate the drilling efficiency by coupling the mechanical specific energy and vibration modes. An ANN is trained with these variables to predict the rate of penetration and rock strength, which are also measured in the experiments to be used as ground truth. The reliability of the framework is demonstrated by testing the validity of the ANN model on samples with various mechanical properties. It introduces a reliable and swift method for determining optimal drilling parameters, supported by a sensitivity analysis to fine-tune the ANN and assess the influence of each parameter on performance. This study demonstrates that ANN could be successfully implemented to predict the rate of penetration and rock strength on a laboratory-scaled drilling rig. The results show that the ANN model accurately predicts training and testing datasets for scoring while drilling multiple layers with a correlation coefficient of 0.966. Integration of in-cutter sensing technology, vibration data, and ANN can be of significant interest and be used on field applications to provide a reliable and rapid decision about drilling efficiency.

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Identification of weak buses for proper placement of reactive compensation through sensitivity analysis using a neural network surrogate model

Cited by 6 publications

References 13 publications

Simplifying Neural Network Based Model for Ship Motion Prediction: A Comparative Study of Sensitivity Analysis

Simplifying Neural Network Based Model for Ship Motion Prediction: A Comparative Study of Sensitivity Analysis

A SVM-based Sensitivity Analysis Approach for Data-Driven Modeling of Ship Motion

Application of artificial intelligence to predict rock strength and drilling efficiency using in-cutter sensing data and vibration modes

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