IoT News, Updates and Insights

Innovating Things

New method to predict life expectancy of machines

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:

Rita BurkertNew method to predict life expectancy of machines