This work presents an experimental comparison of speed sensor fault detection and classification methods in a vector-controlled permanent magnet synchronous motor (PMSM) drive. Three approaches are investigated: a model-based Sliding Mode Observer fault detector and compensator, a multilayer perceptron (MLP), and a convolutional neural network (CNN) fault classifiers. The study is entirely based on experimental results obtained on the dSPACE DS1103 Controller Board, ensuring a realistic and reproducible validation environment. The experiments cover a range of operating conditions (variable speed and load), allowing evaluation of each method’s detection accuracy, reliability, and robustness. Furthermore, the operation of the classifiers is based on a different type of speed estimator—the Model Reference Adaptive System (MRAS)—which enables not only fault classification but also fault compensation for each analyzed system.

The MLP and CNN approaches utilize data-driven techniques to classify faults, while the Sliding Mode Observer provides a model-based reference, enabling direct comparison between signal-based, shallow learning, and deep learning approaches. The findings reveal distinct performance differences, with each method showing particular strengths and limitations under the tested conditions. This comparison highlights the trade-offs between computational complexity, accuracy, and practical applicability, offering guidance for selecting appropriate diagnostic strategies for industrial PMSM drives and other applications.