CNets subsidiary IoT Bridge has in cooperation with KTHs School of Electrical Engineering and Computer Science (EECS) sponsored a thesis that analyses a new method to create a health indicator of machines. It is based on distance measurements transformed into a vector space through a feed-forward neural network. The neural network is trained using a multi-objective optimization algorithm to optimize criteria that are desired in a health indicator. The constructed health indicator is used as input to a gated recurrent unit (a neural network that handles sequential data) to predict the remaining life of a system in question.
The Master thesis Machinery Health Indicator Construction using Multi-objective Genetic Algorithm Optimization of a Feed-forward Neural Network based on Distance was carried out By Jacob Nyman in the field of machine learning: