Diffusion distance improvement-based neighborhood preserving embedding intermittent process fault detection method

Abstract

The invention provides a diffusion distance improvement-based neighborhood preserving embedding intermittent process fault detection method, which mainly comprises the following steps of (1) acquiringintermittent process data of a plurality of batches under normal working conditions to form three-dimensional training data, (2) unfolding the acquired three-dimensional training data into two-dimensional data and performing standardization processing, (3) establishing a neighborhood preserving embedding model based on diffusion distance improvement, and solving a mapping transformation matrix, (4) establishing statistics of a Hotelling statistical model T2 and a square prediction error statistical model SPE under normal data, and solving control limits of the statistics, (5) collecting online intermittent process data to form test data, and performing unfolding and standardization processing on the test data according to the method in the step (2), (6) projecting the preprocessed test data through the mapping transformation matrix obtained in the step (3), and (7) calculating the statistics of the Hotelling statistical model T2 and the square prediction error statistical model SPE of the test data, and judging whether a fault occurs or not.

Description

technical field [0001] The invention belongs to the technical field of industrial process monitoring, and relates to an intermittent process fault detection method based on diffusion distance improvement and neighborhood-preserving embedding. Background technique [0002] The batch chemical process occupies a considerable proportion in modern industry, especially the pharmaceutical and food industries. In recent years, with the increasing market demand for multi-specification and high-quality products, the batch production process is developing in the direction of high efficiency, large scale and integration , so that the safety and reliability requirements of the batch production process are getting higher and higher. Compared with the continuous production process, the batch production process has strong nonlinear, multi-modal, non-Gaussian characteristics due to frequent changes in product and process operating conditions and often in an unstable state. In order to ensur...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that the Neighborhood Preserving Embedding (NPE) algorithm only uses the Euclidean distance to select the nearest neighbor points, the feature extraction is not sufficient, resulting in poor fault detection effect, and provides an improved neighborhood preserving embedding based on the diffusion distance. Process Fault Detection Methods

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