Minimally Supervised Machine Learning for Condition Monitoring

Spanias and Tepedelenlioglu

The aim of this project is to enable low-touch development and deployment of monitoring systems which predict machine failures before they occur. To this end, we will develop standardized work flows which leverage: (1) libraries of predefined solutions allowing for easy definition of normal and abnormal signatures for specific situations; and (2) focus on the use of inertial, magnetic, and pressure sensors. We will investigate methods to select features and specific sensors that provide best results. We will select machine learning algorithms and begin to adapt them to our application.


Arizona State University