Fault classification method based on self-adaption integrated semi-supervision Fisher discrimination
A Fisher discrimination and fault classification technology, applied in electrical testing/monitoring, testing/monitoring control systems, instruments, etc., can solve problems such as unstable performance as supervised learning and semi-supervised learning, and improve monitoring effects, Favorable effects of automated implementation, enhanced mastery
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[0017] The invention aims at the problem of fault classification in industrial processes. In the method, a large amount of unlabeled data is randomly sampled when offline modeling is performed, and a plurality of semi-supervised random training subsets are formed with the labeled data. When training sub-classifiers in each iteration, adaptive weight adjustment of labeled samples is performed, and then semi-supervised Fisher dimensionality reduction is performed to obtain multiple Fisher discriminant matrices (composed of r Fisher discriminant vectors, where r is Dimensions after dimensionality reduction), and use the labeled sample data after dimensionality reduction to obtain the posterior probability matrix, the fusion weight of the sub-classifier and the sample weight of the labeled data in the next iteration according to the Bayesian statistical method. The posterior probability matrix of the labeled data and the corresponding label are used as the training samples of the K...
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