No abstract

The acoustic climate assessment needed for the selection of solutions (technical, legal and organisational), which will help to minimise the acoustic hazards in the analysed areas, is realised on the basis of acoustic maps. The reference computational algorithms, assigned to them, require very thorough preparation of input data for the considered noise source model representing -in the best possible way -the acoustic climate. These input data are burdened with certain uncertainties in this class of computational tasks. The uncertainties are related to the problem of selecting proper argument values (from the interval of their possible variability) for the modelled processes. This situation has a direct influence on the uncertainty of acoustic maps.The idea of applying the interval arithmetic for the assessment of acoustic models uncertainty is formulated in this paper. The computational formalism assigned to the interval arithmetic was discussed. The rules of interval estimations for the model solutions determining the sound level distribution around the analysed noise source -caused by possible errors in the input data -were presented. The application of this formalism was illustrated in uncertainty assessments of modelling acoustic influences of the railway noise linear source on the environment.

Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl PAwlik P. Single-number statistical parameters in the assessment of the technical condition of machines operating under variable load.

The fault diagnosis for maintenance of machines operating in variable conditions requires special dedicated methods. Variable load or temperature conditions affect the vibration signal values. The article presents a new approach to diagnosing rotating machines using an artificial neural network, the training of which does not require data from the damaged machine. This is a new approach not previously found in the literature. Until now, neural networks have been used for machine diagnosis in the form of classifiers, where data from individual faults were required. A new diagnostic parameter rDPNS (Relative Differences Product of Network Statistics) as a function of the machine's shaft order was proposed as a kind of new order spectrum independent of the machine's operating conditions. The presented work analyses the use of the proposed method to diagnose misalignment and unbalance. The results of an experiment carried out in the laboratory demonstrated the effectiveness of the proposed method.

Uncertainty assessment in modelling of acoustic phenomena with uncertain parameters using interval arithmetic on the example of the reverberation time estimation, are presented in the paper. The application of the classical interval analysis formalism as well as its expansions are shown. Statistical methods of estimation of the reverberation time are based on parameters, which are related, among others, to the geometry of the analysed room, characteristics of sound absorption, and interior transmission. Values of these parameters are usually dicult to determine, which has a signicant inuence on the modelling result. The interval analysis allows to determine the variability interval of the parameter being estimated. The authors determined the inuence of the input parameters uncertainty on the estimated reverberation time, calculated according to the Sabine, EyringNorris and MillingtonSette formulae. The uncertainty analysis was performed for the literature data, related to the reverberation time calculations of the room of a certied acoustics. PACS: 43.28.Lv, 43.55.Br