Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills

Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.

Fig. 1. shows the modeling frequency spectral data of the shell vibration, which is used to predict load parameters within ball mill. It is a typical high dimensional, small samples based regression modeling problem. Using the proposed nonlinear frequency spectral feature extraction-based selective ensemble modeling approach, Fig. 2 shows that 2 kernel latent variables (LV) are used to construct prediction model with the best prediction performance.Figure optionsDownload as PowerPoint slide