Systems health monitoring is a method within the concepts of the Internet of Things, which 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 degradation utilizing life cycle loads.Various actual implementations, including the failure of the Boeing 787 battery system, will be given.
