  Prognosis and Health Management (PHM), critical for ensuring task completion
by complex systems and preventing unexpected failures, is widely adopted in
aerospace, manufacturing, maritime, rail, energy, etc. However, PHM's
development is constrained by bottlenecks like generalization, interpretation
and verification abilities. Presently, generative artificial intelligence (AI),
represented by Large Model, heralds a technological revolution with the
potential to fundamentally reshape traditional technological fields and human
production methods. Its capabilities, including strong generalization,
reasoning, and generative attributes, present opportunities to address PHM's
bottlenecks. To this end, based on a systematic analysis of the current
challenges and bottlenecks in PHM, as well as the research status and
advantages of Large Model, we propose a novel concept and three progressive
paradigms of Prognosis and Health Management Large Model (PHM-LM) through the
integration of the Large Model with PHM. Subsequently, we provide feasible
technical approaches for PHM-LM to bolster PHM's core capabilities within the
framework of the three paradigms. Moreover, to address core issues confronting
PHM, we discuss a series of technical challenges of PHM-LM throughout the
entire process of construction and application. This comprehensive effort
offers a holistic PHM-LM technical framework, and provides avenues for new PHM
technologies, methodologies, tools, platforms and applications, which also
potentially innovates design, research & development, verification and
application mode of PHM. And furthermore, a new generation of PHM with AI will
also capably be realized, i.e., from custom to generalized, from discriminative
to generative, and from theoretical conditions to practical applications.
