光滑凝聚函数小波算法在采煤机截齿载荷去噪中的应用

白杨溪; 陈洪月; 陈洪岩; 王鑫

doi:10.13433/j.cnki.1003-8728.20190338

光滑凝聚函数小波算法在采煤机截齿载荷去噪中的应用

doi: 10.13433/j.cnki.1003-8728.20190338

白杨溪^1,,
陈洪月^{1, 2, ,},
陈洪岩¹,
王鑫^{1, 3}

1.
辽宁工程技术大学机械工程学院，辽宁阜新　123000
2.
煤炭工业协会高端综采成套装备动力学测试与大数据分析中心，辽宁阜新　123000
3.
辽宁省高等学校矿产资源开发利用技术及装备研究院，辽宁阜新　123000

基金项目: 国家自然科学基金项目（51874157）、国家自然科学基金青年科学基金项目（52004119）、国家能源研发（实验）中心重大项目（2010_215）及辽宁省教育厅项目（LJ2019QL018）资助

详细信息

作者简介:
白杨溪（1992−），博士，研究方向为机械系统建模与仿真，1105499564@qq.com

通讯作者:
陈洪月，教授，博士生导师，博士，chyxiaobao@126.com

中图分类号: TP274
计量
- 文章访问数: 212
- HTML全文浏览量: 25
- PDF下载量: 7
- 被引次数: 0
出版历程
- 收稿日期: 2019-09-17
- 网络出版日期: 2020-12-08
- 刊出日期: 2020-12-05

Application of Smooth Aggregate Function Wavelet Algorithm in Denoising of Shearer Pick Load

Bai Yangxi^1
,,
Chen Hongyue^{1, 2
, ,},
Chen Hongyan¹,
Wang Xin^{1, 3}

1.
College of Mechanical Engineering, Liaoning Technical University, Liaoning Fuxin 123000, China
2.
Dynamic Test and Big Data Analysis Center for High-end Complete Integrated Coal Mining Equipment, China National Coal Association, Liaoning Fuxin 123000, China
3.
Research Institute of Technology and Equipment for Exploitation and Utilization of Mineral Resources, Liaoning Fuxin 123000, China

摘要

摘要: 针对采煤机截齿载荷中含有噪声问题，提出了基于一种光滑凝聚函数的小波去噪算法，为了更全面地评估去噪效果，构建了综合评价指标。依据截齿载荷信号特点，选取含有噪声的Blocks、Bumps和Leleccum作为测试信号。采用本文方法对测试信号进行去噪处理，经综合评价得到sym5小波基3层分解时去噪效果最佳，并通过横向与软、硬阈值小波去噪算法对比。结果表明：本文方法对应Blocks、Bumps以及Leleccum信号综合评价指标S分别为0.31、021和0.15，相比与传统方法去噪效果提升了0.02~0.09，最后将本文方法应用到截齿载荷信号去噪中，并取得了良好的效果。
- 小波算法 /
- 凝聚函数 /
- 截齿载荷 /
- 综合评价指标
Abstract: Aiming at the problem of excessive noise in shearer pick load signal, a wavelet denoising algorithm based on smooth condensation function was proposed in this paper. In order to evaluate the denoising effect more comprehensively, a comprehensive evaluation index S was constructed. According to the characteristics of pick load signal, noise blocks, bumps and leleleccum were respectively selected as test signals. The denoising effect of the test signal was the best when the sym5 wavelet base was decomposed into three layers by comprehensive evaluation. The results show that this method has the best denoising effect when compared with the soft and hard threshold wavelet denoising algorithms. The synthetical evaluation indexes S of block, bumps and lelccum signals are 0.31, 021 and 0.15 respectively. Compared with traditional methods, the denoising effect of this method is improved by 0.02-0.09. Finally, this method is applied to the denoising of pick load signals and good results are obtained.
- wavelet transforms /
- algorithms /
- pick load /
- comprehensive evaluation index

HTML全文

图 1 测试系统原理图

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图 2 实验现场

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图 3 截齿载荷的时间历程曲线

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图 4 空载噪声概率密度图

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图 5 3种阈值函数对比图（$\lambda = {\rm{1}}{\rm{.5}}$）

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图 6 3个原始测试型号图像

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图 7 3个含噪声的测试信号

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图 8 不同小波基阶数及不同分解层数下测试信号的综合评价指标对比曲线

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图 9 不同阈值函数对含噪声Blocks信号去噪效果对比图

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图 10 不同阈值函数对含噪声Bumps信号去噪效果对比图

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图 11 不同阈值函数对含噪声Leleccum信号去噪效果对比图

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图 12 去噪后的截齿载荷时间历程曲线

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表 1 去噪效果评价表

信号	去噪方法	SNR	RMSE	r	s
Blocks	硬阈值	20.67	213.65	6.65	0.38
	软阈值	22.08	181.66	1.57	0.33
	本文方法	24.70	182.15	1.30	0.31
Bumps	硬阈值	21.53	177.87	0.97	0.23
	软阈值	21.64	177.87	0.97	0.23
	本文方法	23.13	177.76	0.72	0.21
Leleccum	硬阈值	26.60	180.64	7.18	0.25
	软阈值	27.64	180.34	6.19	0.24
	本文方法	32.12	163.49	0.47	0.15

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