This paper presents a novel implementation of a Neural Network model in a low-power embedded system for burst detection in pipelines. It combines a quantized model with the LEA (Low-Energy Accelerator) and FRAM memory to perform signal processing and inference operations, optimizing the balance between accuracy and energy efficiency. The model was trained on a dataset of 187 signals (80 noise, 107 ruptures). Accuracy, precision, and recall metrics were used for model evaluation. After implementation in the microcontroller, a validation protocol was executed to assess the impact of quantization on the method’s accuracy.