Hydraulic shock absorbers are mechanical devices responsible of vibration damping. Although a high level of development and tuning has been carried on, in order to ensure high performance standards in almost every situation, some dynamic phenomenon affecting internal fluid may reduce the damping capacity. In hydraulic shock absorbers, the energy is dissipated forcing the internal fluid through calibrated orifices; this energy is converted in heat and dispersed outwards from the external walls. Hydraulic fluid has therefore a fundamental role for the proper functioning of the overall device, and the most important variations of its chemical-physical properties have to be considered. One of the most dangerous phenomena involving the internal fluid is cavitation, which could affect performances and generate structural damages of the internal components of the damper. The aim of this work is to diagnose the phenomenon of cavitation using experimental data from a prototype of monotube shock absorber equipped with transparent walls and developed for research purposes. The identification of force and displacement parameters is carried out through experimental tests on the prototype, which is a totally adjustable device equipped with pressure and temperature transducers for every chamber. The optical access provided from the transparent wall allows to collect images from a high-speed camera, which could be related to the signals coming from the transducers. This approach is valuable for analyzing the occurrence and the time development of dynamic phenomena of cavitation of the flow or the motion of the main valve blades. Finally, the acquisition of the optical images, coupled with evidences from experimental data, allows to characterize the dynamic events of cavitation, from his onset.
Supervisory Control And Data Acquisition (SCADA) systems have recently become ubiquitous in wind energy technology. SCADA data analysis actually can provide considerable performance improvement at low cost. This also boosts wind energy exploitation, because it enlarges short and long term economic sustainability of investments. Nevertheless, SCADA data analysis poses several scientific and technological challenges, mostly related to the vastness of the data sets required for significant analysis. Separating the signal from the noise is therefore a complex task. In the present work, this issue is tackled by the point of view of state dynamics of wind turbines. SCADA control systems often record superabundant and ambiguous information. Therefore, in this work it is shown that hierarchical classification of information and time discretization of the continuous motion of states are powerful tools. The time-discretized state dynamics is processed in the formulation of several indices for performance evaluation and fault diagnosis. The method is tested on the data set of a wind farm owned by Renvico s.r.l. and sited in Italy.
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