Fault diagnosis method based on non-equal-weighted local preserving embedding

Abstract

The invention discloses a fault diagnosis method based on non-equal-weighted local preserving embedding, and the method aims at solving a problem of how to build a dynamic directed graph structure according to the complex dynamic characteristics of data in a modern industrial process, so as to preserve the local neighbor relation of original samples, to preserve the local structural relation of the original feature variables, and to enable the embedded low-dimensional data structural relation to be consistent with an original space. According to the invention, the method comprises the steps: constructing a directed network through non-equal-weighted connecting sides of the graph, calculating a probability distance guide sample similarity matrix, forming a non-equal-weighted local preserving embedded model, so as to effectively represent the local neighbor relation of samples in a dynamic process; introducing a feature variable to the graph construction, preserving the local relation information of the feature variable, selecting a feature variable which severely affects a process fault, and further improving the classification precision of a diagnosis model. Compared with a neighbor preserving embedding method, the method can represent the topological structure relation of process data, also achieves the construction of the non-equal-weighted local preserving directed graph, achieves the obtaining of the neighbor relation between the samples, represents the local manifold structure of the feature variable in a better way, and reflects the dynamic change conditions of the process. Therefore, the non-equal-weighted local preserving embedded model can obtain a better fault diagnosis result of the dynamic process.

Description

technical field [0001] The invention belongs to the field of industrial process monitoring, and in particular relates to a fault diagnosis method based on unequal weight local preservation embedding, which utilizes the probability distance of neighboring samples to construct a directed map of unequal weight adjacency, while maintaining the neighboring samples and local features of the feature space The variables are consistent with those in the original space, capture the underlying manifold structure of the data, and reflect the changes in the dynamic process. Background technique [0002] The fault diagnosis of modern industrial process plays a pivotal role in ensuring production safety and improving production. With the development of distributed control systems, the production process has a large number of various characteristic variables. These noisy, small-variety fault variables are difficult to diagnose. Therefore, extracting or selecting effective and important fe...

AI Technical Summary

Problems solved by technology

In addition, the goal of NPE is to keep the local structure of the data in the feature space consistent with that in the original space, but it ignores the local preservation ability of the feature variables, which may cause NPE to be unable to effectively capture the structural changes of the map.

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