A degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies -Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

This paper describes the development phases of a numerical-experimental integrated approach aimed at obtaining sufficiently accurate predictions of the noise field emitted by an external gear pump by means of some vibration measurements on its external casing. Harmonic response methods and vibroacoustic analyses were considered as the main tools of this methodology. FFT acceleration spectra were experimentally acquired only in some positions of a 8.5 cc/rev external gear pump casing for some working conditions and considered as external excitation boundary conditions for a FE quite simplified vibroacoustic model. The emitted noise field was computed considering the pump as a 'black box', without taking into account the complex dynamics of the gear tooth meshing process and the consequent fluid pressure and load distribution. Sound power tests, based on sound intensity measurements, as well as sound pressure measurements in some positions around the pump casing were performed for validation purposes. The comparisons between numerical and experimental results confirmed the potentiality of this approach in offering a good compromise between noise prediction accuracy and reduction of experimental and modelling requirements.

Recent research on the improvement of the noise climate at the operator station of construction machines during real working conditions showed that loudness and sharpness are the parameters best correlated to the annoyance sensation. In order to verify the efficacy of some noise control solutions in improving the operator comfort conditions, it is necessary to detect the minimum differences in these metrics which are subjectively perceived: the just noticeable differences. The subjective listening tests were performed following the classical Method of Limits on a jury of subjects tested one at a time. The subjects were asked to detect the just noticeable differences for both loudness and sharpness sensations, the step size of the stimulus being 0.3 sone and 0.02 acum, respectively. The test was repeated at three different signal presentation levels. Results show that the just noticeable difference in loudness becomes greater as the overall sound pressure level of the signal increases. On the contrary, the just noticeable difference in sharpness has very small variations with the overall level. Focusing on the highest presentation level, 75% of subjects perceives a different sensation when sounds have a loudness difference of at least 0.8 sone and a sharpness difference of 0.04 acum.

This paper describes the results of a study aimed at developing and validating a prediction model to assess the annoyance conditions at the operator station of compact loaders by using noise signal objective parameters only. For this purpose, binaural measurements were carried out on 41 compact loaders, both in stationary and real working conditions. The 62 binaural noise recordings were objectively analysed in terms of acoustic and psychoacoustic parameters and then divided into 9 groups and used in specific jury tests to obtain the subjective annoyance scores. Finally, multiple regression technique was applied to the first 6 groups of noise stimuli to develop the model while the remaining groups were used to validate it.