Introduction
Many industrial systems rely on some form of 'health' estimation to predict and to react suitably to malfunctions. The goal of this project is the development, theoretical analysis, and implementation of methods to estimate and predict the health of engineering systems from real-time sensor data.
Explaining the science
The health of an engineering system is defined as the failure probability distribution over all points in time from the current time onwards. The estimation of this distribution poses many challenges of both theoretical and applied nature. For instance, a gradual change in health is the norm, rather than the exception, and traditional anomaly (changepoint) detection methods are often of little use.
An example of industrial 'health' estimation is in aircraft turboengines, where anomalies need to be detected as soon as possible, preferably on the ground, in order to ensure safe and economical flight operations. It is in general a difficult to estimate the health of a complex engineering system accurately and efficiently. In particular, over-reporting in a high-stress environment like an airplane cockpit may overload human operators.
Another aspect of system health is the selection of meaningful statistics of the future failure distribution, in order to guide maintenance and inspection efforts in real-world systems.
Project aims
The goal of this project is the development, theoretical analysis, and implementation of methods to estimate the health of engineering systems from sensor data in real-time and to make predictions about the health in the future.
Idealised theoretical models will be investigated and ideas will be shared with practitioners and applied statisticians to guide the theoretical investigations. While at least in the beginning the emphasis of the project is predominantly on the theoretical side, new algorithms will be implemented and applied to data from industry partners.
This work will allow for better predictions and hence avoidance of failure in complex machinery, and therefore improve the safety of people and property.
Researchers and collaborators
