Chaitanya Sankavaram, Bharath Pattipati, Anuradha Kodali, and Krishna Pattipati
Electrical and Computer Engineering,
University of Connecticut,
Storrs, CT, 06269, USA
Qualtech Systems Inc.,
Putnam Park, Suite 603
100 Great Meadow Road,
Wethersfield, CT, 06109, USA.
Sachin Kumar and Michael Pecht
Center for Advanced Life Cycle Engineering (CALCE)
University of Maryland
College Park, MD, 20742 USA
Recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of vehicles is monitored and managed. Concomitantly, there is an increased trend towards the forecasting of system degradation through a prognostic process to fulfill the needs of customers demanding high vehicle availability. Prognosis is viewed as an add-on capability to diagnosis that assesses the current health of a system and predicts its remaining life based on sensed features that capture the gradual degradation in the operation of the vehicle. This paper discusses a hybrid model-based, data-driven and knowledge-based integrated diagnosis and prognosis framework, and applies it to automotive (suspension and battery systems) and on-board electronic systems.
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