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.