Multi-modal industrial process fault detection method of weighted k-nearest neighbor standardized method

Abstract

The invention relates to the technical field of multi-modal industrial process fault detection, and discloses a multi-modal industrial process fault detection method of a weighted k-nearest neighbor standardized method. The detection method comprises a modelling stage and a detection stage; the modelling stage comprises the following steps: collecting normal data of different modals in the process, and serving the normal data as the integrity to form a training set X belonging to Rnxm. Through the multi-modal industrial process fault detection method of the weighted k-nearest neighbor standardized method, the information from the same modal can be intensified and the different modal information can be weakened in the application process of the WKNS method through the importing of a weightof the distance; and meanwhile, an operation of determining the nearest neighbor parameter k value according to the experience is avoided in the computation process, and the modal effect between different modals and stages is effectively eliminated; and the single-modal independent modelling and the model attributive division for the new testing sample are avoided by combining the WKNS-PCA method;and the traditional single-modal fault detection method is applied, a certain generalization capacity is provided, and the fault detection precision is improved.

Description

technical field [0001] The invention relates to the technical field of multimodal industrial process fault detection, in particular to a multimodal industrial process fault detection method based on a weighted k-nearest neighbor standardization method. Background technique [0002] In recent years, industrial process fault detection methods based on statistical theory have developed rapidly. Experts have also proposed different solutions for the different characteristics of complex processes. However, these methods are mostly applied in the assumption that the production process is in a single steady state. However, in the actual industrial production process, the production process may change due to the demand for product characteristics or the adjustment of the production structure of the enterprise. The multi-modal or multi-stage process structure will also cause the process data to conform to different modal distributions. This will limit the use of single-mode process f...

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Problems solved by technology

[0005] Aiming at the deficiencies of the prior art, the present invention provides a multi-modal industrial process fault detection method based on a weighted k-nearest neighbor standardization method, which has the advantages of high fault detection accuracy and solves the problem of low fault detection accuracy

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