D-matrix Based Fault Modeling for Cryogenic Loading Systems

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 datasets derived from two automotive systems, namely hybrid electric vehicle regenerative braking system, and an electronic throttle control subsystem simulator. 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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Two-Phase Flow Modeling of Cryogenic Loading Operations

Devine¹,

Luchinsky²,

Foygel³

et al. 2019

Heat and Mass Transfer - Advances in Science and Technology Applications

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We consider problem of modeling and controlling two-phase cryogenic flows during ground loading operations. We introduce homogeneous moving front and separated two-phase flow solvers that are capable of fast and accurate online predictions of flow dynamics during chilldown and transfer under nominal conditions and in the presence of faults. Concise sets of cryogenic correlations are proposed in each case. We present results of application of proposed solvers to the analysis of chilldown in large-scale experimental cryogenic transfer line build in Kennedy Space Center. We discuss optimization of parameters of cryogenic models obtained using general inferential framework and an application of the solvers to the fault detection and evaluation based on D-matrix approach. It is shown that solver's predictions are in good agreement with experimental data obtained for liquid nitrogen flow in nominal regime and in the presence of faults.

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D-matrix Based Fault Modeling for Cryogenic Loading Systems

Cited by 3 publications

References 12 publications

A wireless multiparameter cryogenic monitoring method using passive backscatter sensors

A wireless multiparameter cryogenic monitoring method using passive backscatter sensors

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

Two-Phase Flow Modeling of Cryogenic Loading Operations

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