The objective of this paper is to describe the optimization of a hybrid renewable energy system. The system is intended to supply a residential neighborhood with thermal and electrical power. A software based model has been developed to serve as an experimental tool for calculations and comparison. The aim of the research is to find an optimal size per each element of the system due to the demand to keep the system as independent from the public grid as it is possible. The proposed system model consists of a combined heat and power plant, thermal boiler and a photovoltaic array with an additional lithium-ion battery for electrical energy storage. A methodology based on optimal power production for maximal covering of consumer`s demands is applied in a combination with approximate prizes per natural gas, prizes per purchased and sold back electrical energy from and to the utility grid. Results formed as graphs and comparable cases are expected as outcomes, based on the most optimal calculations for lowest net present cost per purchases from the utility grid.

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In a motorized spindle, due to the complexity of the system and nonlinear relationship between features and types of faults, it is difficult and inefficient to use traditional methods or physical models for the fault diagnosis. This paper focuses on the research on applying Radial Basis Function (RBF) Networks for fault detection and classification in the motorized spindle. As a data driven model with high efficiency, RBF networks has the advantage solving the nonlinear problems and dealing with the contradictory samples in the training process. In this research, the data, including rotating speed, temperature, and acceleration signals with three axes (X, Y and Z), are collected from a dynamic balancing platform to evaluate the working condition and detect the potential faults of the motorized spindle.