迁移学习在超声波流量计数据分析及故障诊断上的应用

Application of Transfer Learning in Data Analysis and Fault Diagnosis of Ultrasonic Flowmeter

摘要: 超声波流量计作为一种重要的工业计量设备，被广泛应用在流体检测方面。不同设计的流量计采集不同的特征，在每时每分每处产生大量的数据，这些数据相似度不一、结构不定。传统的机器学习无法高效处理这种不同分布的学习，需要应用迁移学习方法。本文使用迁移学习处理不同流量计产生的类似数据，在数据相似度较高的情况下进行了迁移学习，实验获得了较好的效果。

Abstract: As an important industrial measuring equipment, ultrasonic flowmeter is widely used in fluid de-tection. Flowmeters with different designs collect different characteristics and generate a large amount of data at every time, every minute and every place. These data have different similarities and structures. Traditional machine learning cannot efficiently use such data. This kind of problem requires transfer learning method. In this paper, we deal with similar data generated by different flowmeters, transfer learning under the condition of higher data similarity, and obtain better results.