Dataset

Neural networks provide useful approaches for determining solutions to complex nonlinear problems. The use of these models offers a feasible approach to help aircraft maintenance, especially health monitoring and fault detection. The technical complexity of aircraft systems poses many challenges for maintenance lines that need to optimize time, efficiency, and consistency. In this work, we first employ Convolutional Neural Networks (CNN), and Multi-Layer Perceptron (MLP) for the classification of aircraft Pressure Regulated Shutoff Valves (PRSOV). We classify a wide range of defects such as Friction, Charge and Discharge faults considering single and multi-failures. As a result of this work, we observed a significant improvement in the classification accuracy in the case of applying neural networks such as MLP (0.9962) and CNN (0.9937) when compared to a baseline KNN (0.8788).