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 problem of automatic reliability monitoring and reliability-centered maintenance is increasingly important today. In this paper, we compare the accuracy of four machine learning approaches for fault detection in a hydraulic system. The first three approaches are based on SVM classifiers with linear, polynomial and RBF kernels and the last one is a gradient boosting on oblivious decision trees. We evaluate algorithms on the synthetic dataset generated by our simulation model of the helicopter hydraulic system and show that high accuracy fault detection can be achieved.
This article discusses a sampling algorithm for machine learning in order to capture the trend of the cumulative deterioration of the characteristics of a hydraulic pump (cumulative degradation), which affects the efficiency of its operation and manifests itself in the form of a drop in volumetric efficiency. To generate data, a simulation model of a typical station for the supply of working fluid in technological complexes, developed in the SimulationX program, is used. The transient processes of pressure change in the system are described, from the analysis of which a tendency of a decrease in the average component of the pressure signal is traced, which is used as a diagnostic feature - an indicator of the state of the system. An example is also considered that describes the possibility of assessing the residual life of the system based on data characterizing the past state of the system, and can be adapted when forming a more complex base, taking into account the use of artificial neural networks.
In order to increase efficiency of diagnostics of electro-hydro-mechanical systems (EHMS) it is advisable to have simulation models of typical faults. Such approach makes it possible to estimate in advance, even at the stage of mathematical modeling, the impact of different faults on functioning of hydraulic systems. This work is aimed at creating a database containing complexes of diagnostic features, which allow distinguishing types of faults, their causes and stages of development. In the paper, typical faults of EHMS are presented on the basis of statistical information from literature sources and experimental research. They include internal and external leakages, spool and sleeve sticking, degradation of power fluid. The causes of faults and their impact on hydraulic systems functioning are considered. Simulation models of typical faults are implemented and studied in the SimulationX software package. The static and dynamic characteristics of the systems are investigated in order to identify diagnostic signs of various faults. The impact of typical faults on various system parameters is discussed. During the research, the tasks of selecting the rational location of sensors of different types (pressure, flow, displacement, or force sensors), their quantity for recognition of a typical fault are solved. The results of theoretical and experimental studies of serviceable and faulty systems for cases of control and disturbance actions are presented. Comparative analysis of transient processes of serviceable and faulty EHMS is presented with assessment of difference between theoretical and experimental data. The results of the work allow to more rationally designing the diagnostic complex for more accurate identification of the type of fault, stage of its development and prediction of residual service life of EHMS.
Gas pressure regulators are widely used in gas transportation and distribution systems. They are designed for deep pressure reduction and maintainance with high accuracy over a wide flow range. Operation at a high pressure drop is accompanied by a high level of noise, for reduction of which, silencers are used. However, installation of a noise suppressor into the regulator design has a significant impact on its static and dynamic characteristics. This can lead to a decrease of accuracy, loss of stability and occurrence of self-oscillations of the valve. These, in turn, lead to increasing noise and vibration, wear of contact surfaces and premature failure of the regulator. The paper presents results of a study of dynamic characteristics of a modernized serial regulator with a built-in noise suppressor. A mathematical model was compiled and its study was carried out in the SimulationX software package. The joint influence on the system stability of the parameters of the muffler and the block of throttles, designed to adjust the static characteristic of the regulator, is considered. It is shown that the proper choice of throttle resistances can ensure the stability of the control system in a wide range of gas flow rates. The results can be used when designing regulators with built-in noise suppressors.
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