2 results about "Kernel sparse representation" patented technology

The invention discloses a multi-working-condition environment-oriented adaptive dictionary kernel sparse representation process monitoring method, which aims at the problems of feature forgetting and insufficient nonlinear modeling when working conditions change continuously, dynamically adjusts feature space mapping through kernel function parameters, and combines macroscopic dictionary evolution with microcosmic key feature base alignment, so as to monitor the sparse representation process of the adaptive dictionary kernel. Adaptive dictionary modeling under multiple working conditions is realized, sparse coefficients are solved in a high-dimensional kernel space to generate monitoring residual errors, and finally an efficient nonlinear process monitoring system is constructed.

The application discloses a bearing intelligent diagnosis method based on a generalized domain data fusion strategy and kernel sparse representation, designs a generalized domain data fusion strategy for dictionary learning, specifically uses an improved Kalman filter fusion framework to project time domain and frequency domain signals to a generalized domain state space and realizes signal adaptive fusion, and secondly, in order to avoid the influence of time shift characteristics on a dictionary learning model, develops a kernel discriminative sub-dictionary learning method, specifically uses a Gaussian kernel function to map the fused generalized domain signals to a high-dimensional feature space, then learns a specific category kernel discriminative sub-dictionary in a data-driven manner through a kernel K-SVD algorithm, then uses the learned specific category kernel discriminative sub-dictionary to realize sparse representation of unknown bearing signals in a high-dimensional space, and finally realizes intelligent identification of the bearing health state according to a minimum reconstruction error criterion. The application enhances the sparse representation ability and discriminative feature mining ability of the dictionary model for nonlinear data.