With increase in the number of sensors installed on sub-assemblies of industrial components, the amount of data collected is rapidly increasing. These data hold information in the areas of operation of the system and evolution of health condition of the components. Therefore, extracting the knowledge from the data can bring about significant improvements in the aforementioned areas. This dissertation provides a path for achieving such an objective. It starts by analyzing the data at the sub-assembly level of the components and creates four frameworks for analysis of operation and maintenance (O&M) for past, present and future horizons at the component level. These frameworks allow improvement in operation, maintenance planning, cost reduction, efficiency and performance of the industrial components. Next, the dissertation evaluates whether such models can be linked with system level analysis and how providing such a link could provide additional improvements for system operators. Finally, preventive maintenance (PM) in generation maintenance scheduling (GMS) in electric power systems is reviewed and updated with recent advancements such as connection to the electricity market and detailed implementation of health condition indicators into the maintenance models. In particular, maintenance scheduling through game theory in deregulated power system, for offshore wind farm (OWF) and an islanded microgrid (MG) are investigated. The results demonstrate improvements in reducing cost and increasing profit for the market agents and system operators as well as asset owners. Moreover, the models also deliver an insight on how direct integration of the collected operation data through the developed component level models can assist in improving the operation and management of maintenance for the system.