Distributed collaborative prognostics


The principal aim of this research project is to enable industrial machines with communication, deep-learning and decision making capabilities in order to improve failure prediction in a cost-effective way. Current prognostics frameworks usually rely on models that operate in a centralised architecture. However, industrial fleets of machines are highly heterogeneous and dynamic, conditions known to be conducive for distribution.



This project combines survival-based deep learning techniques for machine prognostics with Multi-Agent System architectures to predict machine failures in real time. Prognostics is done locally in each asset using a LSTM Recurrent Neural Network designed for regression. This neural network can use a loss function based either in a Gaussian or a Weibull posterior. Learning between the agents in the machine fleet is done by sharing data among machines that are similar to each other. 



Dr Ajith Parlikad



"La Caixa" Fellowship, Siemens


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