tailieunhanh - Gas Turbines Part 7

Tham khảo tài liệu 'gas turbines part 7', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Gas Turbine Condition Monitoring and Diagnostics 139 poses the similar questions. To make one diagnosis this technique joins data from successive measurement sections of one transient and in this regard looks like multipoint diagnosis. From a theoretical and practical standpoint it would be interesting to find out how much these two approaches differ in accuracy. The investigation to answer the questions has been conducted in Loboda Feldshteyn et al. 2007 for the GT1 and an aircraft engine called GT3. The following conclusions were drawn. First a total diagnosis accuracy growth due to switching to the multipoint diagnosis and data joining from different steady states is significant. It corresponds to a decrease in the diagnosis errors by 2-5 times. Second the main effect of the data joining consists in an averaging of the input data and smoothing of the random measurement errors. It is responsible for the main part of the total accuracy growth. If variations in fault description at different operating points are slight as for the GT1 the averaging effect is responsible for the whole growth. Under these conditions the generalized classification has a certain advantage as compared to the conventional one-point diagnosis. Third if the variations are considerable GT3 they give new information for the fault description and produce an additional accuracy growth for the multipoint option. This part depends on the class type but in any case it is essentially smaller than the principal part. The diagnosis at transients may cause further accuracy growth of this type. However it will be limited and the averaging effect will be a principal part of the total accuracy growth relative to the one-point diagnosis. We complete here the descriptions of the studies in the area of diagnosis fault identification based on pattern recognition. In the next section it will be shown how to extend the described approach on the problem of gas turbine monitoring fault detection . 5. Integrated

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