Online Oil debris detection is a complex and systemic issue that multiple requirements should be met for a practical application. When severe wear or tear happens, more accurate detection is needed for higher debris concentration. Aiming at promoting detection accuracy under the practical high debris concentration conditions, in this work, an experimental study is carried out to test the performance of an inductive oil debris detection method using a multi-channel sensor based on dual excitation sources with serial layout. Ferrous powders with different sizes and amounts are used for the experiment. Data processing methods including band-pass filter and anti-aliasing techniques are used for optimizing the estimation. The tested results indicate that the counting accuracy of coarse iron powder with 2.5 g/L concentration hydraulic oil at a flow rate of 40 L/min is higher than 85.8% and by data processing, the accuracy is promoted by 21.4% averagely.
Experimental study on inductive method for online material loss detection with high debris concentration
Zio E.;Shi J.;
2019-01-01
Abstract
Online Oil debris detection is a complex and systemic issue that multiple requirements should be met for a practical application. When severe wear or tear happens, more accurate detection is needed for higher debris concentration. Aiming at promoting detection accuracy under the practical high debris concentration conditions, in this work, an experimental study is carried out to test the performance of an inductive oil debris detection method using a multi-channel sensor based on dual excitation sources with serial layout. Ferrous powders with different sizes and amounts are used for the experiment. Data processing methods including band-pass filter and anti-aliasing techniques are used for optimizing the estimation. The tested results indicate that the counting accuracy of coarse iron powder with 2.5 g/L concentration hydraulic oil at a flow rate of 40 L/min is higher than 85.8% and by data processing, the accuracy is promoted by 21.4% averagely.File | Dimensione | Formato | |
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