An engineering student tests the contamination levels
The test fixture in the closed position.
Degradation and contamination of the working fluids is a major cause of failures in hydraulic systems. Increases in contaminant levels and changes in fluid properties can be both an indicator of deteriorating component conditions and a cause of component failure. Low cost sensors that give early warnings of fluid problems could add significant value to hydraulic systems by providing an opportunity to take corrective action prior to failure.
Robustness in sensing fluid contaminants in real-world operating environments is difficult because of the variability in the fluid state and the variety of possible contaminants. While there are plenty of examples of sensors measuring the conductance or dielectric constant of oils, those sensors either do not acquire multiple measurements across the dielectric spectrum or do not use multivariate models to predict parameters of interest. As such, current technologies lack robustness or the ability to discriminate by contaminant type because of the limited data collected.
With single frequency dielectric measurements, the measured response is a combination of the primary parameter of interest and any other interference parameters. In single frequency measurements, the fundamental assumption is that the response of the primary parameter is orders of magnitude greater than the response of any possible interference parameter, which is then assumed to be insignificant. In many cases, this assumption does not hold true, and the interferences can significantly affect measurement accuracy.
The multi-frequency approach is based on the premise that the primary and inference factors respond differently at different frequencies, allowing the separation of the response from the primary parameter of interest from the interferences. In fact, these sensing systems can often be configured to simultaneously measure multiple parameters. The proposed approach collects much more information about a target fluid, multiple dielectric measurements across a wide spectral range, thus providing a greater sensing capability.
Technology developments for the wireless market can be exploited in this approach for the purpose of detecting high contaminant levels in fluids. In addition, the project’s approach employs the use of computational intelligence techniques (such as genetic algorithms) for multivariate analysis of the high dimensional dielectric measurements. These techniques have been shown to produce robust calibration and classification models which will be extremely valuable for the sensor’s design.
In previous work funded by NFPA, it was shown that water, metal and silicon dust contamination levels can be sensed using dielectric spectroscopy. Dielectric measurements are sensitive to fluid temperature, but when temperature was included as a known parameter, the models showed potential to predict contaminant levels.
These experiments were performed using a laboratory impedance analyzer to demonstrate proof-of-concept. The next step is to design the sensor circuitry and cavity so the technology can be practically applied. The goal of this project is to develop a practical low-cost contaminant sensor for hydraulic fluids based on these previous results. This sensor will measure fluid dielectric properties at multiple frequencies based on previous work to detect water, iron and dust contaminants using a simultaneous multi-frequency dielectric measurement technology similar to that developed by the research team in previous work. The sensor architecture will enable the automatic modulation between different sets of frequencies for detecting different contaminants.
This sensor design approach is inherently low cost because no high cost materials or special manufacturing techniques are required. The sensor will require the design of a dielectric sensing cell through which the fluid being measured flows. The proposed sensing technology is leveraging electronic hardware (high speed digital-to-analog and analog-to-digital conversion, embedded signal processors) used in wireless device markets, which has a cost advantage due to the large volumes associated with these markets.
For more information about this research grant or any of the other grants awarded through the NFPA Education and Technology Foundation, please contact the NFPA at 414-778-3354.