Information Fusion Technology Based Feature Extraction Method for Ball Mill Load Measurement

doi:10.11930/j.issn.1004-9649.2014.12.45.3

Abstract

Abstract: The noise signals of ball mills contain important information on mill loads and are critical for mill condition determination However. because of the load sensitivity variety of their frequency spectrum,it is difficult to acquire the accurate and reliable information of load change. In this paper, a novel load feature extraction method is proposed. This method adopts the multi sensor consistency check algorithm to extract the mill noise characteristic frequency bandsthat are sensitive to the load response and obtain the mill load characteristic information by following the least square principle to fuse the scale value of characteristic frequency bands. The noise signals are gathered on site and used to take the simulation test on the new approach. The test results prove that the new method is effective and reliable.

Key words: power plant, mill noise, mill load, consistency measuring, characteristic information

CLC Number:

TP274

WANG Jing-cheng, REN Zhi-wen, ZHOU Zhao-liang, SHU Jin. Information Fusion Technology Based Feature Extraction Method for Ball Mill Load Measurement[J]. Electric Power, 2014, 47(12): 45-48.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2014.12.45.3

https://www.electricpower.com.cn/EN/Y2014/V47/I12/45

References

[1] MOYS MH. The measurement of parameters describing the dynamic behaviour of the load in a grinding mill[C]// International Conference On Mineral Science and Technology, 1984: 205-219.
[2] MOYS MH. Measurement of slurry properties and load behaviour in grinding mills[C]// Applied Measurements in Mineral and Metallurgical Processing IFAC, Transvaal, South Africa, 1988: 3-10.
[3] MULAR AL, BURKERT A. Automatic control of semiautogenous grinding(SAG) circuits[C]// International Conference On: Autogenous and Semiautogenous Grinding Technology, 1989: 651-668.
[4] 司刚全,张蕊,刘建民,等. 自寻优-模糊自适应采样PI控制在钢球磨煤机负荷控制中的应用[J]. 热力发电,2007,36（11）：52-55.
SI Gang-quan, ZHANG Rui, LIU Jian-min, et al . Application of self-optimizing and fuzzy self-adapting sampling PI control in mill load control system of thermal power plant[J]. Thermal Power Generation, 2007, 36(11): 52-55.
[5] 王靖程,孟维东,曹晖,等. 钢球磨煤机中贮式制粉优化控制系统的开发与应用[J]. 热力发电,2007,36（8）：79-83.
WANG Jing-cheng, MENG Wei-dong, CAO Hui, et al . Development and application of optimized control system for coal pulverizing system with intermediate storage bunker for ball mills[J]. Thermal Power Generation, 2007, 36(8): 79-83.
[6] 武桐,李鹏飞,司刚锋,等. 火电厂磨煤机负荷检测方法[J]. 热力发电,2010,39（9）：78-80.
WU Tong, LI Peng-fei, SI Gang-feng, et al . Detection method for coal pulverizer load in thermal power plants[J]. Thermal Power Generation, 2010, 39(9): 78-80.
[7] 王靖程,任志文,舒进,等. 基于最小均方误差准则的ICA过程监控方法[J]. 热力发电,2013,42（11）：121-126.
WANG Jing-cheng, REN Zhi-wen, SHU Jin, et al . A minimum mean square error based novel process monitoring method with independent component analysis[J]. Thermal Power Generation. 2013, 42(11): 121-126.
[8] 孙丽华,曲莹军,张彦斌,等. 钢球磨煤机负荷检测方法的研究及实现[J]. 热力发电,2004,33（11）：25-27.
SUN Li-hua, QU Ying-jun, ZHANG Yan-bin, et al . The research and implementation of ball mill load measurement[J]. Thermal Power Generation. 2004, 33(11): 25-27.
[9] 赵登峰,王国强,许纯新. 基于小波神经网络的球磨机煤位监测研究[J]. 矿山机械,2003,31（10）：14-16.
ZHAO Deng-feng, WANG Guo-qiang, XU Chun-xin. The research of ball mill load measurement based on wavelet neural network[J]. Mining & Processing Equipment, 2003, 31(10): 14-16.
[10] KANG ES, GUO YG, DU YY, et al . Acoustic vibration signal processing and analysis in ball mill[C]// The Sixth World Congress on Intelligent Control and Automation, Dalian, China, 2006: 6690-6693.
[11] 沙毅,曹英禹,郭玉刚. 磨煤机振声信号分析及基于BP网的料位识别[J]. 东北大学学报,2006,27（12）：1319-1323.
SHA Yi, CHAO Ying-Yu, GUO Yu-Gang. Analysis of acoustic signal and BP neural network-based recognition of level of coal in ball mill[J]. Journal of Northeastern University(Natural Science). 2006, 27(12): 1319-1323.
[12] SI Gangquan, CAO Hui, ZHANG Yanbin, et al . Experimental investigation of load behaviour of an industrial scale tumbling mill using noise and vibration signature techniques[J]. Minerals Engineering, 2009, 22(15): 1289-1298.
[13] 王靖程,诸文智,张彦斌. 基于面积的改进灰关联度算法[J]. 系统工程与电子技术,2010,32（4）：777-779.
WANG Jing-cheng, ZHU Wen-zhi, ZHANG Yan-bin. Improved algorithm of grey incidence degree based on area[J]. Journal of Systems Engineering and Electronics, 2010, 32(4): 777-779.
[14] XU Lijun, ZHANG Jianqiu, YAN Yong. A wavelet-based multi- sensor data fusion algorithm[J]. IEEE Transactions on Instrumentation and Measurement, 2004, 53(6): 1539-1546.

[1]	Mingbing LI, Qiang LI, Xiyang GUAN, Haoyang ZHOU, Rui LU, Yankun FENG. Market Oriented Low-Carbon Optimal Scheduling of Virtual Power Plants Considering Multiple User-Side Resources Coordination [J]. Electric Power, 2025, 58(2): 66-76.
[2]	Ke YANG, Dong WANG, Da LI, Wangjun ZHANG, Ga XIANG, Jun LI. Network Security Risk Assessment Index System and Calculation for Virtual Power Plant [J]. Electric Power, 2024, 57(8): 130-137.
[3]	Hai QIN, Sheng CHEN, Honglue ZHANG, Tian XIA, Qinfeng MA, Zhanyu DUAN, Wei YAN. Time-based Equivalent Method for Reactive Power Regulation Capacity of Grid-connected Photovoltaic Plant [J]. Electric Power, 2024, 57(2): 27-33.
[4]	Zhongkai YI, Langbo HOU, Ying XU, Yongfeng WU, Zhimin LI, Junfei WU, Teng FENG, Liu HAN. Aggregation and Operation Key Technology of Virtual Power Plant with Flexible Resources in Electricity Market Environment: Review [J]. Electric Power, 2024, 57(12): 82-96.
[5]	Jinliang ZHANG, Zeping HU. Inventory Optimization Model of Biomass Power Plant Considering Multiple Uncertainties [J]. Electric Power, 2024, 57(12): 157-168.
[6]	Jiangfeng ZHANG, Song KE, Wenjin CHEN, Tianyu WANG, Keke ZHENG, Jun YANG. Virtual Synchronous Control Frequency Regulation Strategy for Adjustable Self-standby Rate in Photovoltaic Plants [J]. Electric Power, 2024, 57(11): 108-118.
[7]	Tianqi SONG, Zhipeng LV, Zhenhao SONG, Yunting MA, Zhihui ZHANG, Shan ZHOU, Hao LI. Research and Thinking on the Aggregation and Dispatching Control Framework of Virtual Power Plant's Large Scale Flexible Resources [J]. Electric Power, 2024, 57(1): 2-8.
[8]	Ying ZHOU, Xuefeng BAI, Yang WANG, Min QIU, Chong SUN, Yajie WU, Bin LI. Analysis and Evolution Trend of Temperature-Sensitive Loads for Virtual Power Plant Operation [J]. Electric Power, 2024, 57(1): 9-17.
[9]	Rui ZHU, Yiding OU, Xiaotian LI, Xingyu LEI, Yuqing ZHOU, Poyang ZHANG, Rui OU. Evaluation of Virtual Power Plant Flexibility Resources Based on External Characteristic Equivalence [J]. Electric Power, 2024, 57(1): 30-39.
[10]	Mengfei XIE, Gaoquan MA, Bin LIU, Zhenning PAN, Yunfeng SHANG. Virtual Power Plant Quotation Strategy Based on Information Gap Decision Theory [J]. Electric Power, 2024, 57(1): 40-50.
[11]	Yunchen FENG, Heping JIA, Min YAN, Genzhu LI, Le LIU, Dunnan LIU. Operation Optimization Method for Virtual Power Plant Participating in Clean Heating Based on Time-of-Use Tariff of Wind Power [J]. Electric Power, 2024, 57(1): 51-60.
[12]	Ting ZHOU, Yudong TAN, Jin SUN, Ming WEN, Jing LIAO, Yang LI, Gong LIU, Dunnan LIU. Collaborative Optimization Strategy for Virtual Power Plant Participating in Energy and Ancillary Service Market [J]. Electric Power, 2024, 57(1): 61-70.
[13]	Shuhan ZHANG, Qian AI, Xiaolu LI, Di WANG. Improved PBFT Consensus Mechanism for Multi-virtual Power Plant Transactions [J]. Electric Power, 2024, 57(1): 71-81, 157.
[14]	Ying ZHANG, Yan WEN, Yu JI, Juan ZUO, Wenbo WANG. Calculation of Day-Ahead Frequency Regulation Capacity for Virtual Power Plants Based on Analytical Target Cascading Method [J]. Electric Power, 2024, 57(1): 82-90.
[15]	Chao ZHANG, Dongmei ZHAO, Yu JI, Ying ZHANG. Real Time Optimal Dispatch of Virtual Power Plant Based on Improved Deep Q Network [J]. Electric Power, 2024, 57(1): 91-100.