An Inference-based Prognostic Framework for Health Management of Automotive Systems

Chaitanya Sankavaram, Anuradha Kodali, Krishna Pattipati, Satnam Singh, Yilu Zhang, and Mutasim Salman
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Full Paper
ijphm_16_009.pdf892.48 KBJune 20, 2016 - 10:31am

This paper presents a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic time-series data. The framework employs proportional hazards model and a soft dynamic multiple fault diagnosis algorithm for inferring the degraded state trajectories of components and to estimate their remaining useful life times. The framework takes into account the cross-subsystem fault propagation, a case prevalent in any networked and embedded system. The key idea is to use Cox proportional hazards model to estimate the survival functions of error codes and symptoms (probabilistic test outcomes/prognostic indicators) from failure time data and static parameter data, and use them to infer the survival functions of components via soft dynamic multiple fault diagnosis algorithm. The average remaining useful life and its higher-order central moments (e.g., variance, skewness, kurtosis) can be estimated from these component survival functions. The framework is demonstrated on two automotive systems, namely, a hybrid electric vehicle regenerative braking system, and an electronic throttle control system. Although the proposed framework is validated on automotive systems, it has the potential to be applicable to a wide variety of systems, ranging from aerospace systems to buildings to power grids.

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Submission Keywords: 
Data-driven and model-based prognostics
applications: automotive
multiple fault diagnosis
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Industrial applications
Modeling and simulation
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