Model-based and data-driven prognosis of automotive and electronic systems

• Published in: 2009 IEEE International Conference on Automation Science and Engineering pp. 96 - 101
• Main Authors: Sankavaram, C., Pattipati, B., Kodali, A., Pattipati, K., Azam, M., Kumar, S., Pecht, M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2009
• Subjects: Automotive engineering; Availability; Communications technology; Computer interfaces; Degradation; Demand forecasting; Hardware; Remote monitoring; Technology management; Vehicles
• ISBN: 1424445787; 9781424445783
• ISSN: 2161-8070
• DOI: 10.1109/COASE.2009.5234108

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.