Prognostics and health management is a method within the concepts of the Internet of Things, that permits the assessment of a system under its actual application conditions. It integrates sensor data with models that enable in-situ assessment of the “health” (e.g. deviation or degradation) of a system from an expected normal operating condition and also predicts the future state of the system based on current and historic conditions. This presentation discusses some methods used for anomaly detection and prognostics, including the monitoring and reasoning of parameters that are precursors to impending “failure”, such as shifts in performance parameters; and the modeling of stress and damage utilizing life cycle loads (e.g., usage, temperature, vibration, radiation). Examples of implementation methods and results are given.