Machine Learning: Diagnostics and Prognostics

Prognostics and health management (PHM) has emerged as an essential approach for preventing catastrophic failure and increasing system availability by reducing downtime, extending maintenance cycles, executing time repair actions, and lowering life‐cycle costs. This chapter provides a basic understanding of data‐driven diagnostics and prognostics. It reviews recent advancements of diagnosis and prognosis techniques with a focus on their applications in practice. The chapter discusses research opportunities that can lead to further improvement of PHM in both theory and practice. Bagging, also known as bootstrap aggregating, is an ensemble learning method that uses a series of homogeneous or heterogeneous machine learning algorithms to improve classification performance. Adaptive boosting is the first practical boosting algorithm and aims to convert a set of weak classifiers into a strong one sequentially. Prognostic techniques can be categorized into two groups: regression analysis and particle filtering.