A review of the role of prognostics in predicting the remaining useful life of assets

Abstract

In this research we present the state of the art in prognostics and health management, demonstrating opportunities and challenges in designing and implementing prognostic models via three case studies. The remaining useful life prediction for Li-ion batteries utilising data analysis is shown to be within 5% accuracy and in fusion prognostic instance 3-5% accuracy when applied to subsea cables. Empirical data gathered in electromagnetic relay lifecycle analysis demonstrated how low rate sampling can classify failure modes with abnormal resistance spikes representing a precursor, 1.5 million actuations, prior to failure. Results demonstrate that due to the agile nature of PHM models and their accuracy, PHM will be vital to resilient and sustainable complex systems.