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Cavitation in kinetic pumps causes lowering performance, mechanical damage and increase of vibrations and noise. Therefore, it is important to detect inception and development of cavitation phenomenon in the pump. This paper deals with signals of vibrations and noise, which will be used for detection and monitoring of cavitation in kinetic pumps, and also to prevent the effect of cavitation in the pump and pumping system. When the cavitation is increasing, the flowing conditions are changing, which leads to an increase of vibrations of the pump and emitted noise in the surroundings. Because vibrations and noise are transferred from the pump through its casing, the signal is non-uniformly distorted due to transfer losses and structure of the casing surfaces. Even so, it is possible to determine development of cavitation phenomena from the measured signal. Noise and vibrations are increasing steadily, but in some specific frequency ranges the signal is more pronounced than in other part of the spectrum. Experimental results have shown that when the cavitation is fully developed, the measured signals at a characteristic frequency or range of frequencies start to decrease. This characteristic frequency or band of frequency is discussed in this paper, and also comparison between theoretical expectations and measurement results was performed.

The most frequently used arc welding process is gas metal arc welding (GMAW). Different methods are in use for monitoring the quality of a welding process. In this paper sound generated during the GMAW process is used for assessing and monitoring of the welding process and for prediction of welding process stability and quality. Theoretical and experimental analyses of the acoustic signals have shown that there are two main noise-generating mechanisms; the first is arc extinction and arc ignition having impulse character, the second is the arc itself acting as an ionization sound source. A new algorithm based on the measured welding current was established for the calculation of emitted sound during the welding process. The algorithm was verified for different welding condition, different welding materials and different specimen. The comparisons have shown that the calculated values are in good agreement with the measured values of sound signal.

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