Planning maintenances and operations is an important concern in power systems. Although optimization based joint maintenance and operations scheduling is studied in the literature, sudden disruptions due to random generator failures are not considered. In this paper we propose a stochastic mixed-integer programming approach for integrated condition-based maintenance and operations scheduling problem for a fleet of generators with explicit consideration of unexpected failures. The proposed stochastic program is based on failure scenarios derived from the remaining lifetime distributions of the generators, as well a chance constraint to ensure a reliable maintenance plan. We propose a deterministic safe approximation of the difficult chance constraint. The huge number of failure scenarios are handled by a combination of sample average approximation and an enhanced scenario decomposition algorithm in a distributed framework. We introduce a number of algorithmic improvements by exploiting the polyhedral structure of the problem, utilizing its time decomposability and an analysis of the transmission line capacities. Finally, we present a case study demonstrating the significant computational benefits of our solution approach, and the importance of considering unexpected generator failures.