This paper examines the effectiveness of neural network algorithms for hydraulic system fault detection and a novel neural network architecture is suggested. The proposed gated convolutional autoencoder was trained on a simulated training set augmented with just 0.2% data from the real test bench, dramatically reducing the time needed to spend with the actual hardware to build a high-quality fault detection model. Our fault detection model was validated on a test bench and showed accuracy of more than 99% of correctly recognized hydraulic system states with a 10-s sampling window. This model can be also leveraged to examine the decision boundaries of the classifier in the two-dimensional embedding space.
The paper presents a test rig equipped with control, measuring and registration hardware for the examination of dynamics and power efficiency of a hydraulic drive with a digital flow controller as one of the most promising means of control. The results of experimental research of a hydraulic drive under digital control in the form of a switched inertance device are presented.
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