  The concept of a digital twin has exploded in popularity over the past
decade, yet confusion around its plurality of definitions, its novelty as a new
technology, and its practical applicability still exists, all despite numerous
reviews, surveys, and press releases. The history of the term digital twin is
explored, as well as its initial context in the fields of product life cycle
management, asset maintenance, and equipment fleet management, operations, and
planning. A definition for a minimally viable framework to utilize a digital
twin is also provided based on seven essential elements. A brief tour through
DT applications and industries where DT methods are employed is also outlined.
The application of a digital twin framework is highlighted in the field of
predictive maintenance, and its extensions utilizing machine learning and
physics based modeling. Employing the combination of machine learning and
physics based modeling to form hybrid digital twin frameworks, may
synergistically alleviate the shortcomings of each method when used in
isolation. Key challenges of implementing digital twin models in practice are
additionally discussed. As digital twin technology experiences rapid growth and
as it matures, its great promise to substantially enhance tools and solutions
for intelligent upkeep of complex equipment, are expected to materialize.
