Siemens (Russia)

Global trend to distributed energy generation results in growing interest to efficient, compact and autonomous small-scale energy equipment. One of the best and proven solutions available for power range below 1MW is high-speed oil-free turbomachines with foil bearings. Such maintenance-free energy systems life-time is mostly determined by proper design of bearing and cooling systems. At prototype development stage various layouts should be analyzed to select the best configuration for given customer. Thus fast and reasonably accurate estimation of heat loads is more important than time-consuming determination of precise values. Bearing system is one of the main sources of heat in small-scale high speed turbomachines. Despite intensive experimental and numerical investigation of various foil bearing configurations performance parameters there are no proven correlations for friction losses available in open sources. In order to solve to some extent this problem simple first-principle based scaling procedure for friction losses in radial and axial-thrust bearings estimation is proposed. Presented approach allows recalculating experimentally measured foil bearing performance parameters to any arbitrary geometry and operation conditions using dimensionless analysis of friction losses and load capacity. Average deviation from available experimental data is within 10-15% range that is acceptable for initial prototyping of bearing and cooling systems.

In this chapter, the authors investigate (1) the popularity of the Theory of Inventive Problem Solving (TRIZ) around the world, (2) how industry currently uses TRIZ to develop new products/processes, and (3) why TRIZ, although highly effective, does not yet enjoy the recognition it deserves. This research reveals that TRIZ is still one of the least known and least popular innovation methodologies in the world. Furthermore, world interest in TRIZ is declining and, while TRIZ does have a recognized niche in the innovation process, it is limited to solving technical problems. The overall conclusion is: TRIZ is approaching the maturity, if not stagnation stage of evolution, and further development should include (1) the development of business-oriented TRIZ tools and (2) close integration with existing best industry practices for developing new products/processes.

Condition monitoring is an important and challenging task actual for many areas of industry, medicine and economics. Nowadays it is necessary to provide on-line monitoring of the complex systems status, e.g. the steel production, in order to avoid faults, breakdowns or wrong diagnostics. In the present paper a novel machine learning method for the automated condition monitoring is presented. Neural Clouds (NC) is a novel data encapsulation method, which provides a confidence measure regarding classification of the complex system conditions. The presented adaptive algorithm requires only the data which corresponds to the normal system conditions, which is typically available. At the same time the fault related data acquisition is expensive and fault modeling is not always possible, especially in case one is dealing with steel production, power stations operation, human health condition or critical phenomena in financial markets. These real word applications are also presented in the paper.