Different techniques are developed for model order reduction such as truncated balanced realization in which the model order reduces based on the Hankel sin- gula r values of the system. System abstraction is widely used to reduce order of models by decreasing the number of states in a dynamical model and their associated parameters thus, simplifying the design and calibration processes. There fore, root causing subsystems with feedback loops requires analyzing downstream and upstream components as well that increases the number of signals required for the analysis. The signal abstra ctio n meth od is ap- plied and successfully tested for abstracting signals in the fuel control system, with high degree of interconnection between software and hardware, using data from more than 5000 con- nected vehicles.Īctuat or3. The proposed label propagation method eliminates the require- ment for a priori known correlation kernel that is needed for a regr essi on analysis. A nov el labe l prop agat ion meth odolo gy is propo sed to sel ect the most relevant signals for the root cause analysis by detecting linear and nonlinear correlations between an observed mal- function and candidate test signals of the control system. In this paper, an abstraction method is presented to identify the most important signals for a root cause analysis by leverag- ing data collected from a connected fleet of field vehicles. High leve l of experti se and detailed knowledge of the underlying software and hard- ware are typically required to analyze a large list of variables and precis ely identi fy the root cause of the malfunct ion. The int eg rat ion of lar ge- scale software with many hard ware comp onent s, how eve r, have increased the complexity of diagnosis and root cause analy sis for a dete cted malfun ctio n. T oday’s automotiv e control systems have gained huge advan- tage from using integrated software and hardware to reliably man age the per for man ce of veh icl es.
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