Deep Surrogate of Modular Multi Pump using Active Learning

TL;DR

This paper presents an active learning framework to accurately estimate the surge distance of a Modular Multi Pump with minimal sensor data, addressing data scarcity and improving estimation performance in energy applications.

Contribution

It introduces a novel active learning approach tailored for pump surge distance estimation, reducing data requirements and enhancing estimation accuracy.

Findings

Active learning effectively estimates surge distance with limited data.

The method improves estimation accuracy compared to traditional approaches.

Results demonstrate practical applicability in real energy systems.

Abstract

Due to the high cost and reliability of sensors, the designers of a pump reduce the needed number of sensors for the estimation of the feasible operating point as much as possible. The major challenge to obtain a good estimation is the low amount of data available. Using this amount of data, the performance of the estimation method is not enough to satisfy the client requests. To solve this problem of scarcity of data, getting high quality data is important to obtain a good estimation. Based on these considerations, we develop an active learning framework for estimating the operating point of a Modular Multi Pump used in energy field. In particular we focus on the estimation of the surge distance. We apply Active learning to estimate the surge distance with minimal dataset. Results report that active learning is a valuable technique also for real application.