The paper presents a possible method to diagnose a mechanical fault of an automotive system. Starting from the point of view that every fault of a mechanical system should introduce an abnormal component within the signal that describes the time history of a mechanical parameter we tried to find a way to reveal it.We were performing some tests involving a military vehicle with respect to the performances of its braking system. The tests were aiming at identifying a way to bring up-to-date the old weapon system from the braking systems point of view. During these tests we observed some anomalies concerning the pressure evolution within the braking cylinders of the vehicle. Some unusual but also systematic noises occurred. As a main issue at this point, the source of the noise should have been identified and filtered if necessary. We had to decide whether the noisy component of the signal is just a noise that should be removed by filtering the signal or it is a physical component of the mechanical parameter itself (not noise but a useful information).These procedures take time and they also request accurate knowledge as well as fine expertise in automotive testing. Since our Dept. has a long and rich practice in this respect, we assumed to processing data and give them a thorough interpretation. So, the first thing we did was to perform a frequency analysis, using classical methods. Usually, a simple frequency analysis cant provide information about a time variation of the frequency spectra due to the Fourier Transforms behavior, since it freezes the signal in time. A much more accurate analysis is the time-frequency analysis. However, observing both the amplitude and power spectra can lead to a useful conclusion. We concluded that the noise we met within the signal is due to the brake drums loss of circular shape (they turned into an oval, the process being known as ovalization). Hence, we cant talk about a noise as it is usually defined.
The present paper aims to analyze the influence of certain parameters specific to the distribution systems on a truck engine performance. The research is focused on how the change in the shape of lobes of a camshaft can influence the vehicle functional parameters. It was considered a non-variable fuel distribution system which represents by itself the controlling part, needed to move the poppet valves in order to ensure engine operating cycles. There have been compared results obtained in the case of different profiles of lobes, taking into account the accelerations during movements, in order to obtain minimum inertia of the distribution system components. By increasing the total height of the lobes, it was observed an increase in engine power and torque. Also, the intake air efficiency was improved by approximately 4%. Furthermore, engine fuel consumption reached higher levels in the case of the modified camshaft, in order to increase engine power.
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.
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