Multi-condition soft sensor modeling of domain adaptation partial least squares based on hypergraph regularization
Multiple conditions in industrial processes can lead to changes in data distribution,which in turn can cause traditional soft sensor models to become inaccurate.Therefore,this paper proposes a domain-adaptive multi-conditions soft sensor regression model framework based on the hypergraph regularization.First,the nonlinear iterative partial least squares algorithm is used as the basic model to reconstruct the current condition data by using historical condition data in the latent variable space,to enhance the correlation between conditions and effectively reduce the differences in data distribution;Meanwhile,a low-rank sparsity constraint is imposed on the reconstructed coefficients to preserve the local and global subspace structure of the data;Secondly,the domain-adaptive latent variable solving process is constrained by the hypergraph regularterm,which effectively avoids the data structure being destroyed in the process of searching for latent variables.Finally,the model parameters are optimized by using the alternating direction multiplier method.Experiments on multiple datasets show that the method can effectively improve the prediction accuracy and generalization performance of the soft sensor model under multiple working conditions.
multiple working conditionshypergraphstructure preservationdomain adaptationsoft sensor
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