Multi-source Mechanical Signal Analysis Based on Linear Latent Structure Model for Mill Load Parameter Forecasting

Accurate detect mill load inside the heavy rotating ball mill is one of the key factors for realizing operation optimization and control of the mineral grinding process. By using multi-source mechanical signals, such as vibration and acoustical signals at different location of the ball mill system, to construct mill load parameter forecasting (MLPF) model has been a hot and focus recently. However, a few researches about the relative accuracy and reliability contribution ratio of these different multi-source signals are addressed. It is necessary to select the suitable mechanical signal at different industrial applications. Aim to this problem, multi-source mechanical signal analysis based on linear latent structure MLPF model is studied in this paper. At first, Fast Fourier transformer (FFT) is used to obtain mechanical frequency spectrum of the time domain vibration and acoustic signals with characteristics of high dimension and strong co-linearity. Then, project to latent structure (PLS) models are build based on these spectra data. Finally, a new defined metric is used to estimate relative accuracy and reliability contribution ratio of these multi-source signals based on generalization performance and structure parameter of different prediction model. Experiments based on a laboratory-scale ball mill are used to valid the proposed method.