Fault detection method based on Daubechies wavelet transform and elastic network

Abstract

The invention relates to a fault detection method based on Daubechies wavelet transform and an elastic network. The method includes the steps that training data and test data are obtained, and the test data are standardized; Daubechies wavelet transform is carried out on the training data, each set of the data serves as a pivot element column vector, elastic network regression is carried out on the pivot element column vectors and the training data, and different minimum evaluation values beta en are solved; by means of a probability density evaluation method, the optimal evaluation value beta en is solved to serve as a threshold value; Daubechies wavelet transform and elastic network regression are sequentially carried on the test data, and the solved evaluation value beta en of each set of the data is compared with the threshold value, and whether faults exist in the data is judged. Compared with the prior art, the method has the advantages that all feature values are taken into consideration, detection accuracy is improved, and applicability is good.

Description

technical field [0001] The invention relates to the field of intelligent information processing, in particular to a fault detection method based on Daubechies wavelet transform and elastic net. Background technique [0002] With the rapid development of modern industry and science and technology, the capability and modernization level of the system are increasing day by day. In order to achieve more functions and better meet people's needs, the investment and scale of projects are also increasing, and the complexity of the system is also increasing. However, this increases the probability of failure a lot. Once a key part fails, it will cause huge property losses and casualties. So how to detect and eliminate the fault in time is particularly important. In recent years, with the rapid development of computer technology and the widespread application of Distributed Control System (DCS) in industrial processes, a large amount of process data has been collected and stored. T...

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Problems solved by technology

However, the above methods rely too much on the analysis of variance of process data. Since the noise of real continuous process data often has highly non-Gaussian and nonlinear characteristics, these data dimensionality reduction detection methods based on analysis of variance inevitably make some important features missing or missing. Then there is an error in the detection process

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