Multi-stage fermentation process fault monitoring method based on self-adaption FCM algorithm

Abstract

The invention discloses a multi-stage fermentation process fault monitoring method based on a self-adaption FCM algorithm. The multi-stage fermentation process fault monitoring method based on the self-adaption FCM algorithm solves the following problems that clustering of multi-batch three-dimensional data can not be achieved, the number of divided stages needs to be appointed manually, the center of clustering is initialized at random, and the method is prone to being affected by sample noise and jump points when the standard FCM algorithm is used for dividing stages in the fermentation process. The method comprises the specific steps that firstly, similarity indexes of all time data matrixes are calculated to serve as clustering input samples, an initial clustering center set is obtained according to the maximum and minimum clustering rules, and then a clustering effectiveness function is introduced to determine the optimal number of clusters through the self-adaption iteration. The method achieves the division of the stages of the fermentation process based on multiple normal operation batch data, so that the stage division process is more objective and accurate, a staged modeling monitor model reduces the false alarm rate and false negative rate of faults, and the method has the important significance for achieving control over the fermentation process and fault detection.

Description

technical field [0001] The invention relates to the technical field of MSPM-based batch process monitoring and fault diagnosis, in particular to a method for establishing a multi-stage fault monitoring model by applying an improved stage division algorithm in the fermentation process and implementing online monitoring. Background technique [0002] The fermentation process is a common production method in the modern process industry, and is widely used in the production of medicine, wine making, and biochemical products. The fermentation process not only has the characteristics of time-varying, large inertia, correlation, and uncertainty of general nonlinear systems, but also because some important parameters in the fermentation process, such as bacterial concentration and product concentration, cannot be measured online, so the fermentation Process control is more complex than general nonlinear systems. Due to the complex mechanism of the fermentation process and poor data...

AI Technical Summary

Problems solved by technology

The most typical one is the FCM clustering algorithm. However, there are still some problems that have not been solved when this algorithm is used for the stage division of batch processes: the clustering of multi-batch 3D data cannot be realized; the number of clusters needs to be given in advance, and there is no clear Metrics; very sensitive to initialized cluster centers; susceptible to noise and outliers

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