PLSaoNET: A Generalized ANN Model Under PLS Statistical Constraints for Industrial Sensing

The application of artificial neural network (ANN) models to achieve higher accuracy in industrial sensing has become a popular research topic in recent years. However, neural network models are purely data-driven multivariate “black-box” models, and the features extracted from the hidden layer have no actual physical meaning, making the performance of ANN-based sensing models unstable and difficult to practically apply at process industry sites. To address these challenges, this paper proposes a generalized ANN model called the partial least squares (PLS)-assisted optimization network (PLSaoNET). PLSaoNET employs the PLS model to assist in determining the initialization weights of the network and the number of hidden-layer neurons. The subsequent training serves as a reoptimization process guided by the PLS regression result, enabling the network to incorporate statistical constraints and thereby reducing its reliance on data. In addition, to address the problem of uneven distributions of sample labels at industrial sites, this paper designs a stratified sampling method for network retraining. The efficiency and superiority of the proposed method are verified via two industrial sensing applications: the monitoring of iron grade in iron ore concentrate slurry samples based on laser-induced breakdown spectroscopy (LIBS) data, and the assessment of the quality of diesel fuels based on near-infrared (NIR) spectroscopy data. In comparison with a PLS regression model and a Xavier initialization-based backpropagation neural network (BPNN) model, PLSaoNET exhibits the best modeling accuracy and generalization performance. This work designs a complete theoretical framework to guide the determination of hyperparameters and specify the solution paths of the network, thereby satisfying the triple requirements of accuracy, robustness, and ease of use in industrial processes. The proposed model holds great potential for improving the accuracy and reliability of industrial sensing in production processes.