Industrial process fault detection method based on wavelet transform and Lasso function

Abstract

The invention relates to an industrial process fault detection method based on wavelet transform and a Lasso function. The industrial process fault detection method comprises the steps of (1) obtaining normal data and fault data from a Tennessee and Eastman industrial process model, carrying out standardization processing on the obtained data, (2) carrying out wavelet transform on the normal data, compressing the normal data, carrying out Lasso regression between each set of training data processed through wavelet transform and a training data matrix in the mode that each set of training data is used as a pivot element column vector, obtaining different minimum estimated values (please see the symbol in the specification), (3) obtaining the optimal minimum estimated value (please see the symbol in the specification) through a probability density estimation method, using the optimal minimum estimated value as a threshold, and (4) sequentially carrying out wavelet transform and Lasso regression on test data, comparing the minimum estimated value (please see the symbol in the specification) obtained from each set of test data with the threshold, and judging whether each set of test data has a fault or not. Compared with the prior art, the industrial process fault detection method based on wavelet transform and the Lasso function has the advantages that all the eigenvalues are taken into consideration, and detection accuracy is improved.

Description

technical field [0001] The invention relates to an industrial process fault detection method based on wavelet transform and Lasso function, which belongs to the field of intelligent information processing. Background technique [0002] In process and manufacturing industries, efforts are being made to produce high-quality products with low product failure rates to meet ever-increasing, stringent safety and environmental regulations. In order to pursue higher standards, modern industrial processes contain a large number of closed-loop control variables. Designers often design standard process controllers to maintain satisfactory operation by compensating for the effects of disturbances or changes in the process. However, it is these controllers that can malfunction if they do not properly handle process changes. The four steps of process monitoring are fault detection, fault identification, fault diagnosis and process recovery. The method of pattern recognition focuses on d...

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However, it is these controllers that, if not properl

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