A KL(Kullback-Leibler) divergence multi-block moving window slow feature analysis method was proposed to solve the problems that the variable feature information cannot be fully utilized by the traditional block segmentation method based on experience, the local information is ignored by a single modeling method, and the off-line model cannot adapt to the time-varying characteristics. KL divergence was used to measure the distance between variables in the normal working condition data set, and the minimum error sum criterion was introduced to cluster, which was divided into two sub-blocks with the minimum distance. On this basis, the slow feature analysis method was utilized to model each sub-block, and the optimal model was obtained by updating the sampled data with moving window. Monitoring statistics were calculated respectively, and the fault monitoring results were fused with support vector data description to achieve fault diagnosis. The proposed method was applied to the monitoring of Tennessee Eastman process, and higher fault detection rate and lower false alarm rate are obtained, verify the feasibility and effectiveness of this method.

In view of the structural residual noise in the weighted nuclear norm minimization algorithm and the inability to maintain the edge structure of the image, a denoising method that minimizes the weighted kernel norm and improves the wavelet threshold was adopted. The total variation model to perform preliminary denoising of the noise image, and the noisy image to subtract the preliminary denoised image were used. An improved wavelet threshold function was used to denoise the noise difference image obtained after subtraction. The denoised residual image was superimposed with the preliminary denoised image, and the superimposed image was finally denoised using an iterative weighted kernel norm minimization algorithm based on the residual noise level. Compared with the more popular denoising algorithms, the PSNR and SSIM processed by this algorithm are improved, the texture structure of the image can be maintained, and the effect is better in a high-noise environment.