Heterogeneous selective ensemble learning model for mill load parameters forecasting by using multiscale mechanical frequency spectrum

A ball mill is a heavy mechanical device and its safe operation affects the entire grinding process. Mill load is a key index in the optimum operation of the grinding process, but it cannot be measured directly. In industrial practice, operational experts normally estimate its value based on their experiences and the mechanical signals produced by the ball mill. In this paper, we proposed a heterogeneous selective ensemble method by using a multiscale mechanical frequency spectrum. The multicomponent adaptive decomposition algorithm is first used to decompose the original shell vibration and acoustic signals into sub-signals with different timescales. Then, selective ensemble (SEN) kernel projection to latent structure algorithm is used to model the spectral data of these sub-signals. Furthermore, the latent features of multiscale spectra are extracted to construct SEN models based on fuzzy inference. Finally, the two types of heterogeneous SEN models are fused by using information entropy. The main contribution of this study is that the proposed soft-sensing model has a dual-layer ensemble structure that can fuse multi-source information in different mechanical sub-signals with physical meaning. Moreover, the proposed model can simulate the fuzzy cognitive behavior of domain experts in the mineral grinding process. The effectiveness of the method is verified by the shell vibration and acoustical data of a laboratory-scale ball mill.