Azure machine learning studio and SCADA data for failure detection and prediction purposes: A case of wind turbine generator

Abstract

Most industrial systems have supervisory control and data acquisition (SCADA) systems that collect and store process parameters. SCADA data is seen as a valuable source to get and extract insights about the asset health condition and associated maintenance operations. It is still unclear how appliable and valid insights SCADA data might provide. The purpose of this paper is to explore the potential benefits of SCADA data for maintenance purposes and discuss the limitations from a machine learning perspective. In this paper, a two-year SCADA data related to a wind turbine generator is extracted and analysed using several machine learning algorithms, i.e., two-class boosted decision tree, two-class decision forest, k-means clustering on Azure ML learning studio. It is concluded that the SCADA data can be useful for failure detection and prediction once rich training data is given. In a failure prediction context, data richness means ensuring that fault features are presented in the training data. Moreover, the logs file can be used as labelled data to supervise some algorithms once they are reported in a more rigorous manner (timing, description).

Risk Analysis of DNP3 Attacks

“…Such a technological evolution in the industrial domain has brought multiple advantages, such as the increase in efficiency and speed in manufacturing [12] [13]. However, at the same time, the integration of Internet of Things (IoT) devices in such Critical Infrastructures (CIs) has led to an escalation of cyberattacks and incidents in recent years [14] [15].…”