Multi-data fusion power plant fault diagnosis method based on fuzzy clustering analysis
A technology of fuzzy cluster analysis and fault diagnosis, applied in data processing applications, character and pattern recognition, instruments, etc., can solve the problems of lack of quantitative diagnosis methods, increase of diagnosis time, hidden dangers of system safety, etc., and achieve rapid and seamless diagnosis process Strong and accurate diagnosis
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[0023] This method comprises the following steps:
[0024] ①Fuzzy clustering: the first step is standardization, that is, to standardize the training samples of the test, and establish its eigenvalue matrix; the second step is clustering, that is, to select the appropriate distance mode to calculate the fuzzy display matrix of the data, and to obtain the fuzzy display matrix of the data through A certain clustering algorithm clusters the sample data; the third step is to determine the optimal clustering level, that is, to find the best clustering result.
[0025] To complete the modeling process of the fuzzy clustering algorithm is to realize the following three steps;
[0026] Let the number of cluster samples in the population be n, denoted as B 1 ,B 2 ,...,B n ; Each cluster sample corresponds to m quantified indicators, denoted as 1, 2,..., m.
[0027] Step 1: Construct the eigenvalue matrix
[0028] Firstly, the data is standardized. In this paper, extreme value stan...
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