LAUSR

Abstract Ball mill is a heavy mechanical device necessary for grinding. Mill load parameters (MLP) relate to production economic indices and process safety. Mechanical signals of the ball mill are used to estimate MLP by domain experts. However, they can only estimate familiar mills effectively in certain time because of human limitation. A new dual-layer optimized selective information fusion is proposed based on the analysis of the characteristics of mill mechanical signals and cognitive behavior of the domain expert for MLP forecasting (MLPF). An ensemble construction strategy based on multi-component mechanical signals adaptive decomposition is employed to build candidate sub-models by using kernel partial least squares (KPLS). The dual-layer optimization strategy is proposed to build selective ensemble (SEN) KPLS (SENKPLS) with optimized ensemble sub-models and their coefficients, thus realizing the trade-off between prediction accuracies and diversity implicitly. The MLPF models based on SENKPLS are constructed by selective fusion multi-source multi-scale frequency spectral information in terms of the auditory perception process of the simulation domain experts. Results show that the proposed strategy can obtain better forecasting results than other state-of-the-art methods.