A new research grant funded by the energy company Centrica will address the problem of predicting the time to the next failure of equipment.
Every year, large sums of money are spent by Centrica on equipment maintenance, including the “rough gas storage” facility – a two-stage gas compressor train at a natural gas terminal. Sensors are located throughout the terminal and measure various physical features of the system, such as temperature, pressure and rotary speed. It is known that the terminal often breaks and that analysis of these sensor readings could provide insight on what caused a given failure and whether another one may occur in a given time frame.
To address the problem, the project will apply a modern machine-learning technique developed by our Computer Learning Research Centre called conformal predictors. Our team, led by Professor Alex Gammerman, will consider various extension of conformal predictors to conformal clustering and detection of anomalies.