Jung, Daniel (Linköping University), Kleman, Björn (Linköping University), Lindgren, David Nils Henrik (Linköping University), Warnquist, Håkan (Scania CV AB)

Fault Diagnosis of Exhaust Gas Treatment System Combining Physical Insights and Neural Networks

Scheduled for presentation during the Regular Session "Modeling, Estimation, and Control of Internal Combustion Engine- II" (MoBT3), Monday, August 29, 2022, 16:10−16:30, Pfahl Hall 140

10th IFAC International Symposium on Advances in Automotive Control, August 28-31, 2022, Columbus, Ohio, USA

This information is tentative and subject to change. Compiled on April 26, 2024

Abstract

Fault diagnosis is important for automotive systems, e.g., to reduce emissions and improve system reliability. Developing diagnosis systems is complicated by model inaccuracies and limited training data from relevant operating conditions, especially for new products and models. One solution is the use of hybrid fault diagnosis techniques combining model-based and data-driven methods. In this work, data-driven residual generation for fault detection and isolation is investigated for a system injecting urea into the aftertreatment system of a heavy-duty truck. A set of recurrent neural network-based residual generators is designed using a structural model of the system. The performance of this approach is compared to a baseline model-based approach using data collected from a heavy-duty truck during different fault scenarions with promising results.