78 citations · 123 across the 10 of their papers we have counts for
10 papers
Contrastive Siamese Representation Learning for Predictive Maintenance of Electrical Submersible Pumps
Seshu K. Damarla, Xiuli Zhu
Electrical submersible pumps (ESPs) are essential in offshore oil production, where unexpected failures can result in significant operational and financial losses. Accurate predict…
Optimal Transport Image Representation and Deep Covariance Alignment (CORAL) for Control Valve Stiction Detection
Seshu K. Damarla
Control valve stiction is a common cause of unwanted oscillations and poor control-loop performance in industrial processes. Data-driven methods can automatically detect stiction,…
Retrieval-based and Fine-tuned LLM Approaches for Industrial Asset Health Monitoring and Decision Support
Seshu Kumar Damarla, Xiuli Zhu
Industrial plants run many important machines such as pumps, turbines, and compressors. Although engineers can use their experience to identify and diagnose machine problems, trans…
Explainable Probabilistic Machine Learning for Predicting Drilling Fluid Loss of Circulation in Marun Oil Field
Seshu Kumar Damarla, Xiuli Zhu
Lost circulation remains a major and costly challenge in drilling operations, often resulting in wellbore instability, stuck pipe, and extended non-productive time. Accurate predic…
Machine learning for industrial sensing and control: A survey and practical perspective
Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim +7
With the rise of deep learning, there has been renewed interest within the process industries to utilize data on large-scale nonlinear sensing and control problems. We identify key…
Estimation of minimum miscibility pressure (MMP) in impure/pure N2 based enhanced oil recovery process: A comparative study of statistical and machine learning algorithms
Xiuli Zhu, Seshu Kumar Damarla, Biao Huang
Minimum miscibility pressure (MMP) prediction plays an important role in design and operation of nitrogen based enhanced oil recovery processes. In this work, a comparative study o…
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
R. Bhushan Gopaluni, Aditya Tulsyan, Benoit Chachuat +6
Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning.…
Piecewise linear approximate solution of fractional order non-stiff and stiff differential-algebraic equations by orthogonal hybrid functions
Seshu Kumar Damarla, Madhusree Kundu
A simple yet effective numerical method using orthogonal hybrid functions consisting of piecewise constant orthogonal sample-and-hold functions and piecewise linear orthogonal tria…
Novel hybrid function operational matrices of fractional integration: An application for solving multi-order fractional differential equations
Seshu Kumar Damarla, Madhusree Kundu
In the present work, an attempted was made to develop a numerical algorithm by the use of new orthogonal hybrid functions formed from hybrid of piecewise constant orthogonal sample…
New orthogonal hybrid function based numerical method to solve system of fractional order differential equations
Seshu Kumar Damarla, Madhusree Kundu
In this paper, an easy-to-implement and computationally effective numerical method based on the new orthogonal hybrid functions is developed to solve system of fractional order dif…