  Since the depletion of fossil fuels, the world has started to rely heavily on
renewable sources of energy. With every passing year, our dependency on the
renewable sources of energy is increasing exponentially. As a result, complex
and hybrid generation systems are being designed and developed to meet the
energy demands and ensure energy security in a country. The continual
improvement in the technology and an effort towards the provision of
uninterrupted power to the end-users is strongly dependent on an effective and
fault resilient Operation and Maintenance (O&M) system. Ingenious algorithms
and techniques are hence been introduced aiming to minimize equipment and plant
downtime. Efforts are being made to develop robust Prognostic Maintenance
systems that can identify the faults before they occur. To this aim, complex
Data Analytics and Machine Learning (ML) techniques are being used to increase
the overall efficiency of these prognostic maintenance systems.
  This paper provides an overview of the predictive/prognostic maintenance
frameworks reported in the literature. We pay a particular focus to the
approaches, challenges including data-related issues, such as the availability
and quality of the data and data auditing, feature engineering,
interpretability, and security issues. Being a key aspect of ML-based
solutions, we also discuss some of the commonly used publicly available
datasets in the domain. The paper also identifies key future research
directions. We believe such detailed analysis will provide a baseline for
future research in the domain.
