The paper presents the method of determination of two-dimensional probability distribution Pf of crack initiation versus fatigue life N and the fatigue damage parameter : Pf − N − ϭ. The proposed distribution Pf uses parameters of the standard fatigue characteristics and allows calculating fatigue life of elements with heterogeneous stress fields at any probability level. The model was successfully verified on experimental test results.
An embedded time interval data acquisition system (DAS) is developed for zero power reactor (ZPR) noise experiments. The system is capable of measuring the correlation or probability distribution of a random process. The design is totally implemented on a single Field Programmable Gate Array (FPGA). The architecture is tested on different FPGA platforms with different speed grades and hardware resources. Generic experimental values for time resolution and inter-event dead time of the system are 2.22 ns and 6.67 ns respectively. The DAS can record around 48-bit x 790 kS/s utilizing its built-in fast memory. The system can measure very long time intervals due to its 48-bit timing structure design. As the architecture can work on a typical FPGA, this is a low cost experimental tool and needs little time to be established. In addition, revisions are easily possible through its reprogramming capability. The performance of the system is checked and verified experimentally.
Together with the dynamic development of modern computer systems, the possibilities of applying refined methods of nonparametric estimation to control engineering tasks have grown just as fast. This broad and complex theme is presented in this paper for the case of estimation of density of a random variable distribution. Nonparametric methods allow here the useful characterization of probability distributions without arbitrary assumptions regarding their membership to a fixed class. Following an illustratory description of the fundamental procedures used to this end, results will be generalized and synthetically presented of research on the application of kernel estimators, dominant here, in problems of Bayes parameter estimation with asymmetrical polynomial loss function, as well as for fault detection in dynamical systems as objects of automatic control, in the scope of detection, diagnosis and prognosis of malfunctions. To this aim the basics of data analysis and exploration tasks - recognition of outliers, clustering and classification - solved using uniform mathematical apparatus based on the kernel estimators methodology were also investigated