We present the design, implementation, and experimental validation of FailSafe - a generalized methodology for fault detection, identification, and remediation (FDIR) for switching power converters in nanogrids. FailSafe is a dynamical systems approach to FDIR for switching power converters, and can be applied to a broad class of converters and fault types. FailSafe operates as part of the control loop of a switching power converter, and uses the measurements and inputs of the converter to achieve both fault detection and identification (FDI) and fault remediation. In this paper, we present two Modules for FDI - a model-based residual approach and a data-driven multiclass Support Vector Machine (one-vs-one) approach. Moreover, we describe the design of a fault remediation Module by designing optimal control actions in a pre-computed reach-avoid set. We present simulation and experimental results using a prototype nanogrid testbed. Simulation results for the multiclass Support Vector Machine (one-vs-one) FDI Module on a 6-phase interleaved boost converter demonstrate fault detection and identification with a classification accuracy of 98.9% for a current sensor fault and 90.8% for an output capacitor fault. Experimental results for the model-based residual FDI Module on a boost converter demonstrate fault detection and identification in 600 μs for a capacitor fault and 250 μs for a voltage sensor fault.