Automobile sensor fault detection method based on independent component analysis and sparse denoising auto-encoder

Abstract

The invention discloses an automobile sensor fault detection method based on independent component analysis and a sparse denoising auto-encoder. The method comprises the following steps: firstly, obtaining non-Gaussian information in process data by using independent component analysis to obtain independent component components, and extracting main independent components by using a sparse denoising auto-encoder to calculate an I2 index; and obtaining the Gaussian information of the operation data in the residual error space by using the sparse denoising auto-encoder to calculate the H2 index;finally, analyzing a fault detection effect by using fault false alarm rate (FAR) and a false detection rate (MDR) index. Compared with other methods, independent component analysis and the sparse noise reduction auto-encoder are combined, the sparse noise reduction auto-encoder is used in the non-Gaussian part to extract a principal element, and unnecessary signal interference is removed; gauss information in the data is extracted in a residual space by using a sparse denoising auto-encoder, so that the robustness of a process monitoring system is improved, the processing capability of nonlinear data is enhanced, and the accuracy of fault diagnosis is improved.

Description

technical field [0001] The invention belongs to the field of automobile process control, in particular to an automobile sensor fault detection method based on independent component analysis and sparse denoising self-encoder. Background technique [0002] With the continuous development of technology, cars are also constantly updated and iterated, the driving speed is getting faster and faster, and the ride is getting more and more comfortable. The application of various sensors in the car makes the functions of the car more and more complete, and the accuracy and efficiency of the equipment are further improved, but the system is also becoming more and more complex. Cars will inevitably break down during driving. Although the traditional dismantling of car parts to check the degree of damage and coupling between car parts is good for fault location and fault repair, but at the same time The requirements of maintenance personnel are also particularly high, especially when en...

AI Technical Summary

Problems solved by technology

Traditional multivariate statistical analysis methods have many limitations. For example, PCA works under the assumption that the process data is a Gaussian distribution, and is limited by the fact that the data is linearly separable; ICA can obtain more information by using high-order statistics. Non-Gaussian information of process data, but powerless against Gaussian information in it

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