Neural networks with simulated data for the faults detection in hydraulic systems

In this work we explore the effectiveness of common neural network architectures for detecting faults in hydraulic systems. We use a typical hydraulic system stand and its physical simulation to collect a training and validation datasets. For all classifiers, we get an accuracy greater than 90% on simulation data. On experimental data with our best classifier achieve 92% accuracy.