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There are complex mapping relationships among different mill load parameters and multi-scale frequency spectra of ball mill's mechanical vibration and acoustic signals. Aim at to construct an effective and meaningful soft sensor model, how to select interesting input variables of each local-scale frequency spectrum and how to fuse these different multi-scale ones jointly, is still an un-solved open...
Data-driven modeling based on the shell vibration and acoustic signals of ball mills is normally applied to overcome the subjective errors of human inference. Many previously proposed selective ensemble (SEN) modeling approaches are based on “the manipulation of input features” from the multiinformation fusion perspective, which cannot selectively and jointly fuse the information hidden in multiscale...
In many situations, such as medical records of rare diseases, early stages of flexible manufacturing system and continuous industrial process, only small training samples can be obtained to construct prediction model. When modelling with high dimensional spectral data, it is very much difficulty to construct efficient and effective prediction model with such a small sample. This research proposes...
Mill load (ML) estimation plays a major role in improving the grinding production rate (GPR) and the product quality of the grinding process. The ML parameters, such as mineral to ball volume ratio (MBVR), pulp density (PD) and charge volume ratio (CVR), reflect the load inside the ball mill accurately. The relative amplitudes of the high-dimensional frequency spectrum of shell vibration signals contain...
Reliable measurement of the mill load is one of key factors to improve mill productivity, production quality and decrease energy consumption for the grinding process. A multi-source data fusion soft-sensor method is proposed to estimate the operating parameters which present the mill load. Fast Fourier transform (FFT) is used to estimate the power spectral density (PSD) of the vibration signals from...
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