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The goal of this work is to improve availability of operational base-stations in a wireless mobile network through non-intrusive fault detection methods. Since revenue is generated only when actual customer calls are processed, we develop a scheme to minimize revenue loss by monitoring real-time mobile user call processing activity. The mobile user call load profile experienced by a base-station displays a highly non-stationary temporal behavior with time-of-day, day-of-the-week and time-of-year variations. In addition, the geographic location also impacts the traffic profile, making each base-station have its own unique traffic patterns. A hierarchical base-station fault monitoring and detection scheme has been implemented in an IS-95 CDMA Cellular network that can detect faults at - base station level, sector level, carrier level, and channel level. A statistical hypothesis test framework, based on a combination of parametric, semi-parametric and non-parametric test statistics are defined for determining faults. The fault or alarm thresholds are determined by learning expected deviations during a training phase. Additionally, fault thresholds have to adapt to spatial and temporal mobile traffic patterns that slowly changes with seasonal traffic drifts over time and increasing penetration of mobile user density. Feedback mechanisms are provided for threshold adaptation and self-management, which includes automatic recovery actions and software reconfiguration. We call this method, Operational Fault Detection (OFD). We describe the operation of a few select features from a large family of OFD features in Base Stations; summarize the algorithms, their performance and comment on future work.