A complex network of interdependent components is susceptible to percolating faults. Sensor networks deployed for real-time detection and monitoring of such systems require adaptive re-distribution of resources for an energy-aware operation. This paper presents a statistical mechanical approach to adaptive self-organization of a sensor network for detection and monitoring of percolating faults. A complex dynamical system of interdependent components (e.g. computer and social network) is represented as an Ising-like model where component states are modeled as spins, and interactions as ferromagnetic couplings. Using a recursive prediction and correction methodology the sensor network is shown to adaptively selforganize to the dynamic environment and real-time detection and monitoring is enabled. The algorithm is validated on a test-bed simulating the operation of a sensor network for detection of percolating faults (e.g. computer viruses, infectious disease, chemical weapons, and pollution) in an interacting multi-component complex system.