added a project goal
Wear in Fluid Power Systems
Standardized parts like hydraulic accumulators are used in nearly every hydraulic system, in many cases even several. Therefore, even small changes in size and weight of accumulators can save considerable material costs. In mobile applications, hydraulic accumulators are used among others in hydro-pneumatic suspension systems. There is a strong focus on miniaturization and weight reduction, as the components always have to be transported with the vehicle. Energy density and energy content of conventional hydraulic accumulators cannot be maximized at the same time. This limitation can be overcome by adding a heat capacity with large surface into the gas volume of the accumulator. The heat capacity enlarges the isothermal frequency range and therefore enlarges the energy density of the accumulator at the given frequency and the given size. In this paper an experimental comparison of conventional hydraulic accumulators and accumulators with foam inserts shows, that at a specific frequency band, the stiffness of foam filled accumulators is significantly lower than of conventional accumulators. The energy density is about 11 % higher than in conventional accumulators. Consequently, a space reduction of about 18 % is possible.
With increasing digitization, models are more important than ever. Especially their use as soft sensors during operation offers opportunities in cost saving, easy data acquisition and therefore additional functionality of systems. In soft sensor networks there is redundant data acquisition and consequently the occurrence of inconsistent values from different soft sensors is encouraged. The resolution of these data-induced conflicts allows for the detection of changing components characteristics. Hence soft sensor networks can be used to detect wear in system components. In this paper this approach is validated on a test rig. It is found, that the soft sensor network is capable to determine wear and its extent in eccentric screw pumps and valves via data induced conflicts with relatively simple models.
Reliability of sensor information in today's highly automated systems is crucial. Neglected and not quantifiable uncertainties lead to lack of knowledge which results in erroneous interpretation of sensor data. Physical redundancy is an often-used approach to reduce the impact of lack of knowledge but in many cases is infeasible and gives no absolute certainty about which sensors and models to trust. However, structural models can link spatially distributed sensors to create analytical redundancy. By using existing sensor data and models, analytical redundancy comes with the benefits of unchanged structural behavior and cost efficiency. The detection of conflicting data using analytical redundancy reveals lack of knowledge, e.g. in sensors or models, and supports the inference from conflict to cause. We present an approach to enforce analytical redundancy by using an information model of the technical system formalizing sensors, physical models and the corresponding uncertainty in a unified framework. This allows for continuous validation of models and the verification of sensor data. This approach is applied to a structural dynamic system with various sensors based on an aircraft landing gear system.