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 ballistic simulation attempted in this work is among the most difficult as both the projectile and the target experience significant deformations. Traditionally these simulations have been performed using a Lagrangian approach, i.e. a deformable mesh with large mesh deformations. There are three often used techniques when studying ballistic problems with the Lagrangian method: remeshing (generally not available for 3D hexahedra meshes), the 'pilot hole' technique and material erosion. Because these techniques imply element removal, in order to allow the calculation to continue, the Lagrangian method lacks a physical basis. Moreover, no general guidance exists for selecting one of the three techniques mentioned before. The Smoothed Particle Hydrodynamic method as implemented in the commercial code LS-DYNA has been used in this paper to solve the problem of the impact between different caliber projectiles and various types of metal targets. The results are compared to those produced by dynamic analysis using conventional finite element methods with material erosion as implemented in LS-DYNA.

Theoretical developments of certain engineering areas, the emergence of new investigation tools, which are better and more precise and their implementation on-board the everyday vehicles, all these represent main influence factors that impact the theoretical and experimental study of vehicle's dynamic behavior. Once the implementation of these new technologies onto the vehicle's construction had been achieved, it had led to more and more complex systems. Some of the most important, such as the electronic control of engine, transmission, suspension, steering, braking and traction had a positive impact onto the vehicle's dynamic behavior. The existence of CPU on-board vehicles allows data acquisition and storage and it leads to a more accurate and better experimental and theoretical study of vehicle dynamics. It uses the information offered directly by the already on-board built-in elements of electronic control systems. The technical literature that studies vehicle dynamics is entirely focused onto parametric analysis. This kind of approach adopts two simplifying assumptions. Functional parameters obey certain distribution laws, which are known in classical statistics theory. The second assumption states that the mathematical models are previously known and have coefficients that are not time-dependent. Both the mentioned assumptions are not confirmed in real situations: the functional parameters do not follow any known statistical repartition laws and the mathematical laws aren't previously known and contain families of parameters and are mostly time-dependent. The purpose of the paper is to present a more accurate analysis methodology that can be applied when studying vehicle's dynamic behavior.A method that provides the setting of non-parametrical mathematical models for vehicle's dynamic behavior is relying on neuronal networks. This method contains coefficients that are time-dependent. Neuronal networks are mostly used in various types' system controls, thus being a non-linear process identification algorithm. The common use of neuronal networks for non-linear processes is justified by the fact that both have the ability to organize by themselves. That is why the neuronal networks best define intelligent systems, thus the word 'neuronal' is sending one's mind to the biological neuron cell. The paper presents how to better interpret data fed from the on-board computer and a new way of processing that data to better model the real life dynamic behavior of the vehicle.

The paper highlights the main possibilities available when studying how the vehicles engine operate using algorithms specific to the multivariate statistics. A particular example of studying the engines behavior is represented by the diagnosis activity performed onto the vehicle, an activity that a special attention is being paid to throughout the paper. To this purpose, during the tests we have intentionally caused certain malfunctions to the engine. Circuit breakdowns were intentionally caused on various electric circuits that connect sensors and actuators to ECU. Fitting modern vehicles with electronic control systems offers the possibility for computerized approach of various maintenance operations onto its mechatronic components (sensors, actuators). These components are part of those electronic systems. Such an approach includes onboard simulation of various malfunctions that may occur during normal operation of vehicles. The procedure which is currently presented in the paper herein is about generating controlled malfunctions, using the sensors connector, a signal that is specifically varied towards the electronic control unit (ECU). Thus the ECU will interpret that the system that it is managing indicates a vehicle malfunction. .

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