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.

翻译：由于传感器成本高、可靠性高,泵的设计者尽可能减少估计可行操作点所需的传感器数量,主要挑战在于现有数据数量少。利用这一数量的数据,估计方法的性能不足以满足客户的要求。为了解决数据稀缺的问题,获得高质量的数据对于获得良好的估计非常重要。基于这些考虑,我们制定了一个积极的学习框架,用以估计能源领域使用的模块多泵的操作点。我们尤其注重对激增距离的估计。我们运用主动学习来估计激增的距离,使用最低限度的数据集。结果报告指出,积极学习也是实际应用的宝贵技术。