Analysis of the Impact of Vibrations on a Micro-Hydraulic Valve Using a Modified Induction Algorithm

This paper addresses the impact of mechanical vibrations of different frequencies on a particular type of valve. It has been shown that a neural network can be used to compress measurement data and determine the frequency range that is most important in describing the impact of mechanical vibrations on a micro-hydraulic overflow valve. Later, induction decision trees were used for the generated areas, determining key measurement points. The most important areas of dependence are determined using inductive decision trees in induction. The entropy measure is used to determine the most significant attribute. A modified induction algorithm was used for the comprehensive analyses. The analysis carried out in the paper identified the intervals in which the flow rate plays a decisive role for the entire amplitude and frequency spectrum. An analysis was performed for the 200 horizontal ellipsis 900 [Hz] frequency interval of the external driving force, with a harmonic step of 10 [Hz]. The analysis was performed while considering these main valve parameters: the pressure of the overflow valve opening p = 10 [MPa]; flow rate in the valve: 0.6, 0.8 and 1 [dm(3)/min]; stiffness of valve spring c = 7.49 [N/mm]. Plots were presented and for each plot, the most important four intervals were determined. They are hierarchically ordered in the interval range of the whole frequency spectrum range: (coefficient IV)-the most important; (coefficient III)-important; (coefficient II)-less important; (coefficient I)-least important. A test rig and the results of a study on the effect of mechanical vibration on changes in the amplitude-frequency spectrum of pressure pulsations of a micro-hydraulic system, in which a micro-hydraulic relief valve was subjected to mechanical vibration, are presented.

Automated summary

This paper investigates the impact of mechanical vibrations of different frequencies on a specific type of valve. The study demonstrates that a neural network can be employed to compress measurement data and determine the most important frequency range for describing the impact of mechanical vibrations on a micro-hydraulic overflow valve. Induction decision trees are utilized to determine key measurement points and important areas of dependence. An analysis is conducted to identify the intervals in which flow rate plays a decisive role for the entire amplitude and frequency spectrum.