Vibration data recorded by accelerometer fixed on a mechanical coupling were treated using different data processing techniques as estimating general root mean square, partial root means square, mean, variance, partial variance, kurtosis, full Fourier spectra, and partial spectra, as well as using sequential probability ratio test (SPRT). The numerical results of these methods measured on an electromotive drive train-coupling were collected into a common data set, and a search method of deviation from the normal behavior was elaborated. This selects the bad couplings if at least three of the presented qualification parameters crosses either theoretically or experimentally settled limits. It was shown that the confidence level of such classification of good and bad species was better than 95 % compared with the results of thoroughly tested ones by conventional methods.
Nowadays the automotive industry and the motor development are one of the most dynamically developing industries. One solution to the diagnostic systems providing reliability is the acoustic and vibration measurement system, which can indicate and predict a variety of malfunctions after signal processing. The purpose of this experiment is to analyze the effect of the air-fuel equivalence ratio on the vibration components of an internal-combustion engine (ICE) which is a part of the in question measurement system. In the focus of the experiments are the analysis of the time signal, its spectra, and the power content of the signal. With the increment of the air-fuel ratio the amplitudes of the measured signal and its spectral amplitudes showed a downward trend as the RMS values. In addition, certain frequency components disappeared during the actuation of the ICE with an electromotor, so the characterization of the combustion could be come to the front.
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