Application of Fractional Fourier Transform for Prediction of Ball Mill Loads Using Acoustic Signals

Abstract

In cement plant and power plant, ball mills remain in current and widespread use. The load parameter inside a ball mill directly impacts the stability of the production process, the grinding production rate, and the quality of the product in the grinding process. Accurately predicting the load from acoustic signals remains a challenging problem because of the nonlinearity and high dimensions of spectral data. In this paper, the application of fractional Fourier transforms on acoustic signals for estimating mill load parameter was researched. A fractional Fourier transform can give intermediate time-frequency representations by controlling an additional order, and the acoustical frequency spectra in the fractional Fourier domain can provide more information about the load parameters. According to the distribution of acoustic frequency spectra in the fractional Fourier domain, the strategies of predicting ball mill loads were divided into three segments, namely feature extraction, offline modeling, and online monitoring. These techniques included an acoustic signal analysis in different fractional orders, feature extracted based on mutual information and kernel principal component analysis, offline soft measuring modeling compared with other regression models, and online adaptive monitoring based on the optimal fractional order. The experimental investigation of the proposed method demonstrates its effectiveness for estimating mill loads in the fractional Fourier domain by comparing with the result in the Fourier domain.