The paper deals with a new approach in data analysis of a measured mechanical parameter. The classic approach is mainly based on the deterministic statistics that cant cover the whole field of a complete analysis. The stochastic approach, to be used in this paper, offers far more information about the mechanical parameter and can take into account the non-linearity of the signal, eventually, the mechanical parameter itself. Starting from the point of view that, in real life, there is no steady evolution of any parameter, we decide to take into account the importance of the non-linear components of any signal. After e thorough investigation, we hope we could make the difference between the noise, as non-linear components of the measured parameter, and the useful non-linear components (e.g. important shocks, typically met within a vehicles transmission). Using the stochastic modeling procedures, we aimed at issuing comprehensive, accurate and valuable dynamic models of the phenomenon. These models cam be used in a large variety of situations, from describing the process, to evaluating the health of a mechanical system and to controlling a real-time process based on the pre-set models (previously drawing a map of the systems normal behavior and permanently assessing the deviation from it and acting accordingly). The data were measured within the transmission system of a military vehicle. Specifically, we have gathered information about torque and angular speed of different shafts of the driveline. As everybody knows, the power flows within any vehicles transmission in transient modes mainly and it is accompanied by plenty of noise. It is rather challenging to separate (filter) the useful signal form the noise but, on the other hand, it is the only way to achieve useful data. Therefore, a spectral analysis is a must, but not the conventional one, which has its drawbacks, but a multi-spectral one, which is able to insulate the noise. Moreover, starting from the analysis developed with this method, mathematical models, both in discrete and continuos time can be achieved. It is easy to notice that the models that we have achieved are featured by a very good accuracy. We could push the data processing even further, getting generalized models that provide the needs we have mentioned before, with respect to the mapping of a normal (averaged) behavior of a system, to be used in controlling procedures.
The modelling influence of water content to mechanical parameter of soil in analysis of slope stability
