In this paper, a new method for vibration fault diagnosis of large steam turbine-generation is proposed based on theory of adaptive wavelet network (AWN). A special adaptive wavelet network classifier for arbitrary nonlinear system is developed, algorithm of back propagation type is proposed. By means of computer simulation and with applying to fault diagnosis, and the performance of the AWN are compared with that of the conventional multilayer perception. The results show that this method has better diagnostic, faster speed of learning properties and it is an efficient new method. And then some important related elements, which affect the performance of classification AWN is analyzed, conclusions that are of great importance in real application, are obtained.

Sea surface wind speed and significant wave height (SWH) are two basic parameters, in addition to sea surface height, which can be inferred from satellite altimeter measurements. Traditionally, altimeter-derived wind and wave data are less extensively used compared to sea surface height, as they are sometimes considered as by-products of satellite altimetry (in contrast to, for example, the dedicated scatterometer missions for marine wind observations). However, it is clear that altimeter-based wind and wave data have the unique advantage of being concurrent and collocated with each other. Using eight years (1993-2000) of TOPEX altimeter data with unprecedented accuracy and continuity, the 10-, 50- and 100-year return values of global wind speed and SWH are derived, their characteristics are discussed in relation to wind climatology and wind variability. Validations against in situ observations indicate that the uncertainties of altimeter-derived extreme winds and waves are at the levels of 10% and 5%, respectively. These results suggest that satellite altimeter data, with present quality and duration, can be very useful in many aspects of coastal engineering and marine technology such as design of offshore facilities, ship routing, and preparation of other sea-going activities.

[1] More than a dozen of wind speed (U) algorithms have been proposed during the past 2 decades, as a result of a continuing effort to improve altimeter wind measurement. The progress in terms of accuracy, however, is seen to be rather slow. The reported root mean square (RMS) error of prevailing algorithms varies mostly between 1.6 and 2.0m/s for the dominant wind regime. As far as the TOPEX altimeter is concerned, three measured quantities, namely, the radar cross sections from Ku and C band (sKu and sC), as well as the significant wave height (Hs), have been used in previous algorithm developments, resulting in a variety of single-, dual-, and three-parameter model functions. On the basis of the finding of a banded dependency of the U-sKu relationship on sC a new pproach for retrieving altimeter wind speed, termed linear composite method (LCM), is proposed in this study. The LCM model function appears as a set of sC-dependent linear relations between U and sKu. A unique advantage of this approach is that it allows the algorithm to be tuned or expanded for a given range of wind speed without affecting the rest. Over 1.7 million coincident TOPEX/NASA scatterometer (NSCAT) and TOPEX/QuikSCAT data covering a period of 2.5 years are used to adjust the model. Validation against extensive buoy measurements indicates that the LCM algorithm is almost unbiased and has an overall RMS error of 1.56m/s, which is 12% lower compared to the algorithm in operational use [Witter and Chelton, 1991]. In addition, a small (2.5-6%, depending on the reference data set) but significant improvement is found for the LCM when compared to the most recent dual-parameter algorithm [Gourrion et al., 2002].

本文提出了一种在快速暂态过电压作用下，基于准静态电磁场，以单匝中并联导线为单元支路和以两段线匝为单元支路相结合的集中参数混合线圈模型。对相关的参数计算进行了分析，并对以两段线饼为单元支路的线圈模型进行截波冲击下的仿真分析。