Distributed wireless architectures for prognostics is an important enabling step in prognostic research in order to achieve feasible real-time system health management. A significant problem encountered in implementation of such architectures is power management. In this paper, we present robust power management techniques for a generic health management architecture that involves diagnostics and prognostics for a system comprising multiple heterogeneous components. Our power management techniques are based on online dynamic monitoring of the sensor battery discharge profile which enables accurate predictions of when the device should be put into low power modes. In our architecture, low power mode is achieved by run-time sampling rate modification through sleep states. Our experiments with a cluster of smart sensors for a hybrid diagnostics and prognostics architecture show significant gains in power management without severe loss in performance.
How to Cite
battery power management, distributed sensors, sensor network, sensors, signal processing, wireless sensor networks
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