Using Monte Carlo simulations to define what information to collect for Data Driven Prognostics

Using Monte Carlo simulations to define what information to collect for Data Driven Prognostics

Our webinar, Using Monte Carlo simulations to define what information to collect for Data Driven Prognostics, is also available to view here. You will need to supply your contact details in order to view this content.


As vehicles become increasingly connected to the cloud, there are opportunities to predict and anticipate component failures, known as prognostics. Vehicle manufacturers then face a challenge in deciding what data to send up to the cloud, how frequently and how to combine with other data sources.

Ricardo’s Peter Fussey, Control Solutions Lead shares his experience of developing a connected prognostics solution by using a virtual fleet of vehicles. The fleet of vehicles is developed using a Monte Carlo simulation and realistic damage models. During the development of the prognostic algorithms, different data collection approaches were generated by the virtual fleet and assessed by feeding the data through Ricardo’s data analytics algorithms.

This webinar equips the listener with a new approach to define the data flows for vehicle prognostic algorithms. The use of a virtual fleet allows different data scenarios to be examined and the data capture optimised before making the investments in in-vehicle data processing and cloud-based data capture.

The Presenter

Dr Peter Fussey

Dr Peter Fussey

Control Solution Lead, Software Control and Calibration View profile
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