Dynamic Risk-based Asset Integrity Modelling of Engineering Processes

Poorly maintenance scheduling and the resulting downtime are costly. Deliver maximum performance while minimizing costs and risks over the whole life of engineering systems required a developmental transition from traditional maintenance strategies to smart predictive maintenance. This PhD study aimed at maximizing the value realized from complex engineering assets and systems. To this end, statistical modelling and machine learning were established to problems in intelligent maintenance operations, characterized by data-driven innovations.