@ARTICLE{Tang_Wencong_Method_2023,
 author={Tang, Wencong and Zhang, Fangwei and Luo, Xiaoyan and Wan, Junliang and Deng, Tao},
 volume={vol. 39},
 number={No 1},
 journal={Gospodarka Surowcami Mineralnymi - Mineral Resources Management},
 pages={217-233},
 howpublished={online},
 year={2023},
 publisher={Komitet Zrównoważonej Gospodarki Surowcami Mineralnymi PAN},
 publisher={Instytut Gospodarki Surowcami Mineralnymi i Energią PAN},
 abstract={Green mine construction is the main melody of mining development and problems such as safe production, energy saving and consumption reduction need to be solved urgently. The working conditions of the mill are complex in the process of grinding. Aiming at the problems existing in the feature extraction and load prediction of the mill, a signal-processing method based on adaptive chirp mode decomposition (ACMD) and a standardized variable distance classifier (SVD) is proposed. Firstly, the recursive framework of the ACMD method is used to obtain the initial frequency of mill vibration signals. Secondly, the initial frequency is used to reconstruct the high-resolution component of the mill vibration signal through the iterative frame in the ACMD method. The frequency corresponding to the frequency domain peak of the reconstructed signal is then selected as the mill load feature vector. Finally, with consideration to the influence of standard deviation and standardized variable factors on the feature vectors, a standardized variable distance classifier is proposed. The feature vectors of the mill load are input into the SVD model for training, and the state types of the mill load are obtained. The method is applied to the grinding experiment and the results show that the frequency-domain features obtained by the mill vibration signal-processing method based on ACMD-SVD are obvious, which has high accuracy in the identification of mill load types, and provides a new idea for the extraction of mill load features and prediction of the mill load.},
 type={Article},
 title={Method of vibration signal processing and load-type identification of a mill based on ACMD-SVD},
 URL={http://www.czasopisma.pan.pl/Content/126747/PDF/Tang%20i%20inni.pdf},
 doi={10.24425/gsm.2023.144626},
 keywords={feature information, mill load, ACMD, SVD, feature vector},
}