Double-layer integrated type industrial process fault detection method based on modified independent component analysis (MICA)

Abstract

The invention relates to a double-layer integrated type industrial process fault detection method based on modified independent component analysis. The double-layer integrated type industrial process fault detection method mainly solves two problems which are unavoidable in the establishment process of non-Gaussian process fault models: one is how to determine non-quadratic functions so as to measure magnitude of non-Gaussianity, the other is how to select important independent components to establish the models. The double-layer integrated type industrial process fault detection method comprises the steps of: firstly, utilizing all selection possibilities to establish a plurality of MICA fault detection models in sequence; secondly, monitoring the same process data by means of the plurality of MICA fault detection models; and finally, adopting a double-layer Bayesian probability fusion method to integrate different fault detection results into one result, so as to facilitate the final fault decision-making. The double-layer integrated type industrial process fault detection method provided by the invention can minimize the fault missing report rate caused by the wrong selection of the non-quadratic functions or ranking criteria, and greatly improves the reliability and applicability of the corresponding fault detection models.

Description

technical field [0001] The invention relates to an industrial process fault detection method, in particular to a double-layer integrated industrial process fault detection method based on modified independent element analysis. Background technique [0002] Ensuring the production safety of industrial processes and the stability of product quality is a necessary means to improve the profitability of enterprises. Therefore, reliable and accurate fault detection methods are an indispensable part of the entire industrial control system. Considering the large-scale and complex trend of modern industrial process, the data-driven fault detection method has gradually replaced the fault detection method based on the mechanism model, and has become the mainstream technical means in the field of fault detection research. However, the data collected by modern industrial processes usually exhibits non-Gaussian properties, and prior knowledge about process data and possible fault types is...

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Problems solved by technology

However, the types of faults that may occur in the process are unknown, and the historical data available for reference are also very limited. How to determine the non-quadratic function and how to select important independent component components are two key issues to be solved in the field of non-Gaussian process monitoring. sexual problems

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