Data-based on-board diagnostics for diesel-engine NOx-reduction aftertreatment systems

The NOx conversion efficiency of a combined Selective Catalytic Reduction and

Ammonia Slip Catalyst (SCR-ASC) in a Diesel Aftertreatment (AT) system degrades with

time. A novel model-informed data-driven On-Board Diagnostic (OBD) binary classification

strategy is proposed in this paper to distinguish an End of Useful Life (EUL) SCR-ASC catalyst

from Degreened (DG) ones. An optimized supervised machine learning model was used for the

classification with a calibrated single-cell 3-state Continuous Stirred Tank Reactor (CSTR)

observer used for state estimation. The method resulted in 87.5% classification accuracy when

tested on 8 day-files from 4 trucks (2 day-files per truck; 1 DG and 1 EUL) operating in realworld on-road conditions.