EPSRC Centre for Doctoral Training Cloud Computing for Big Data


Jordan Oakley

I graduated from Newcastle University in 2016 with an MMATH degree with first class honours. I then completed a research masters degree in Bayesian statistics at Durham University. My thesis, titled Bayesian Forecasting and Dynamic Linear Models, implemented dynamic models to forecast severe oliguria in order to model and monitor kidney deterioration to identify kidney injury and the possible adverse outcomes associated with kidney deterioration.

PhD title

Reliability analysis in the age of data-centric engineering

In traditional reliability analysis, failure times of items or systems are observed, along with some covariate information (this information is referred to as failure-time data), and the probability of failure of future items or systems over time is estimated.

Using failure-time data to assess the reliability of highly reliable products is often problematic. For a practical testing duration (which there is always pressure to reduce), few or perhaps no failures may occur. In this scenario, we have very little information to infer the reliability of the products. Often, tests are conducted at increased (accelerated) temperatures, pressures or stresses to obtain failure times for these products. However, inferring failure times at normal-use conditions, which requires extrapolation, depends critically on identifying a relationship between the accelerating variable and the failure time distribution.

In the era of big data, sensors placed on items can give almost continuous information about the state of the system. However, the sensors do not record the actual state of an item or system, only measureable quantities which act as proxies for the system state.

Inferring the reliability of the system from these measurements is therefore a challenging statistical problem. Our goal is to perform inference in real-time allowing for corrective actions to be made as data are observed.

We hope the methodology that we will develop has the potential to improve the operation and maintenance of complex engineered systems and structures. It would allow more preventive (rather than corrective) maintenance to be performed, meaning that parts could be replaced prior to, rather than, after, failures occur. This would improve the reliability of the system, potentially reduce damage to the system and hence reduce costs.

More efficient engineered products and systems in the private sector should lead to lower prices for customers. In the public sector, it would hopefully lead to better value for money for the UK taxpayer.


Kevin Wilson