A data-driven diagnosis system is developed for fault diagnosis of a fuel injection system in a heavy-duty diesel truck. Physical insights and standard component modeling are used to derive a structural model of the fuel injection system. Based on structural analysis, a set of data-driven residual generator candidates is derived, both linear and nonlinear models, and trained using nominal training data from a truck. Evaluations of different fault scenarios show that the proposed models can distinguish between different faults and show the potential of utilizing basic physical insights in data-driven fault diagnosis design. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)