The nature of failure data sets poses particular challenges to modellers. Failures, particularly of critical equipment, are rare. Lots of equipment are replaced in whole or in part before end of life. As a result failure data-sets are unbalanced and sparse. Failures are seldom labelled accurately, and there is often no ground truth for validation.

Condition monitoring data, when available, is often collected at different time intervals. Poor quality data results in greater model complexity that at best muddies inference, and at worst misleads inference and produces persistent prediction bias. These contextual issues, if not dealt with rigorously in model selection and validation practice, leads to poor model performance and a loss of trust by decision makers. To manage these risks, this theme is explicitly cross-disciplinary combining the Bayesian statistics, engineering, nonlinear system identification, machine learning and deep learning.