[3S5-GS-2] Machine learning:

[3S5-GS-2-03] Improving machine learning accuracy in plants and industrial equipment through dynamic selection of training data

〇Yusuke Motegi¹, Yoshitaka Suzuki¹, Yukihiro Kawano¹ (1. IHI Corporation)

Keywords:Machine Learning, Semi-Supervised Learning, anomaly diagnosis

IHI is actively working on utilizing operational data from equipment. Our products, such as plants and production equipment, are used in various industries and social activities. Therefore, providing stable and highly accurate analysis results is crucial for enhancing customer value and addressing social issue. However, in cases where the internal state changes dynamically due to shifts in operational modes or the influence of external temperatures, traditional machine learning methods may result in false detections or reduced analysis accuracy. To address this, we have developed a machine learning method that applies technology for dynamically updating learning data. This allows for high-precision analysis by dynamically selecting learning data sets suited to the changing operational environment of the equipment. This method is effective for a wide range of applications, including anomaly diagnosis and improving the operational efficiency of plants and production equipment. This paper introduces an overview of this technology and presents the results of its validation using open data.