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The detection of cavitation in hydraulic components is of great interest because the implosion of bubbles impairs the operating conditions and might cause gradual damage until the system fails prematurely. The cavitation phenomenon was investigated in this study by analyzing accelerometer signals recorded on a proportional spool valve. The approach has been developed using Chaos Theory and a frequency domain study to demonstrate how this phenomenon entails the emergence of irregular and chaotic vibrational dynamics in the oil flow of a directed valve spool.

Articulated rocker-bogie suspension systems have gained a lot of attention since they were used for rovers destined for space exploration on Mars. This type of suspension allows the vertical load to be distributed equally between the wheels, reducing the risk of the most loaded wheels sinking in the case of soft ground. This peculiarity is verified if the rover moves on flat and horizontal surfaces; if the rover moves on uneven surfaces, the suspension still allows the wheels to be kept in contact with the ground, but the load distribution changes. This paper presents the study of the variation in the distribution of the load among the wheels and the rover stability for different ground geometries; the study involves a preventive kinematic analysis of position to define the configuration of the suspension as a function of the ground geometry.

Several studies were conducted previously on fuel and biofuel performance of micro-turbines. The present paper combines experimental and statistical approaches to study the vibrational behavior of a gas micro-turbine supplied with different pure fuels and admixed with rapeseed oils. Experimental tests carried out at different operating conditions have allowed us to build a classification model through using discriminant analysis. The classification model can distinguish the vibrational behavior occurring when the turbine is fueled with kerosene, or pure and admixed diesel with rapeseed oil. Moreover, the methodology has even allowed us to highlight differences in vibrational behavior caused by small amounts of rapeseed oil admixed in the fuel. The model reliability, in terms of Cohen’s kappa, results in optimal data classification.

Computer vision for control is a growing domain of research and it is widespread in industry and the autonomous vehicle field. A further step is the employment of low-cost cameras to perform these applications. To apply such an approach, the development of proper algorithms to interpret vision data is mandatory. Here, we firstly propose the development of an algorithm to measure the displacement of a mechanical system in contactless mode. Afterwards, we show two procedures that use a 3D camera as a feedback in control strategies. The first one aims to track a moving object. In the second one, the information gained from vision data acquisition allows the mechanical system control to ensure the equilibrium of a ball placed on a moving slide.

Using vision devices to get information from mechanical systems is a huge advantage: indeed, having a contactless is a massive benefit since no other sensor affects the system and somehow system keeps its integrity. This paper deals with the use of vision techniques to measure motion of mechanical systems. We have proposed three different methodologies that employ marker and no-marker approaches in combination with a low cost RGB-depth device.

Computer vision for control is a growing domain of research and it is widespread in industry and autonomous vehicle field. Towards the applicability of such approach the development of proper algorithms to interpret vision data is mandatory. Here, we propose an application to drive the movement of a mechanical system for target object tracking through an RGB-depth camera aid.