改进PF算法在煤与瓦斯突出AE信号去噪中的研究
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摘要
煤与瓦斯突出会产生声发射(acoustic emission,AE)信号。针对提取较纯净有效的AE信号问题,提出一种邻域动态调整(D)果蝇算法(fruit fly algorithm,FOA)智能优化粒子滤波(particle filter,PF)的去噪方法。利用果蝇个体表征PF中的每个信号点粒子,优化粒子滤波的重采样过程,并通过动态调整邻域粒子数量来改善果蝇算法的寻优能力和收敛速度。以均方根误差和信噪比为评价指标,对信号采集系统获取的煤与瓦斯突出AE信号分别使用标准粒子滤波、果蝇优化粒子滤波、改进粒子滤波去噪。结果表明,改进粒子滤波法的信噪比提升了15. 3 d B左右,且均方根误差最低。与其他两种方法相比,改进粒子滤波去噪效果最优。
Coal-gas outbursts produce acoustic emission signals( AE). Giving the problem of extracting more pure and effective AE signals,this paper proposed an intelligent optimized particle filter( PF) of neighborhood dynamic adjustment( D) for fruit fly algorithm( FOA). It used each fruit fly individual characterizing signal particle of PF to optimize the resampling process of particle filtering,and adjusting the number of neighboring particles dynamically improved the optimization ability and convergence rate of fruit fly algorithm. Taking the root-mean-square error and signal-to-noise ratio as the evaluation index,from signal acquisition system,it respectively de-noised the AE signals of coal-gas outburst by standard particle filter,fruit fly optimized particle filter and improved particle filter. The experiment results show that the signal-to-noise ratio of the optimized particle filter algorithm is improved by about 15. 3 d B,and the root-mean-square error is the lowest. Compared with other two methods,the improved particle filter has the best de-noising effect.
Coal-gas outbursts produce acoustic emission signals( AE). Giving the problem of extracting more pure and effective AE signals,this paper proposed an intelligent optimized particle filter( PF) of neighborhood dynamic adjustment( D) for fruit fly algorithm( FOA). It used each fruit fly individual characterizing signal particle of PF to optimize the resampling process of particle filtering,and adjusting the number of neighboring particles dynamically improved the optimization ability and convergence rate of fruit fly algorithm. Taking the root-mean-square error and signal-to-noise ratio as the evaluation index,from signal acquisition system,it respectively de-noised the AE signals of coal-gas outburst by standard particle filter,fruit fly optimized particle filter and improved particle filter. The experiment results show that the signal-to-noise ratio of the optimized particle filter algorithm is improved by about 15. 3 d B,and the root-mean-square error is the lowest. Compared with other two methods,the improved particle filter has the best de-noising effect.
引文
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[9]王晶,宋策,杨立保.融合GrabCut的粒子滤波目标跟踪算法[J].仪器仪表学报,2014,35(S2):20-27.(Wang Jing,Song Ce,YangLibao. Grab Cut combined particle filter algorithm for tracking target[J]. Chinese Journal of Scientific Instrument,2014,35(S2):20-27.)
[10]胡士强,敬忠良.粒子滤波算法综述[J].控制与决策,2005,20(4):361-365,371.(Hu Shiqiang,Jing Zhongliang. Overview ofparticle filter algorithm[J]. Control and Decision,2005,20(4):361-365,371.)
[11]王林,吕盛祥,曾宇容.果蝇优化算法研究综述[J].控制与决策,2017,32(7):1153-1162.(Wang Lin,Lyu Shengxiang,Zeng Yu-rong. Literature survey of fruit fly optimization algorithm[J]. Controland Decision,2017,32(7):1153-1162.)
[12]郑晓龙,王凌.随机资源约束项目调度问题基于序的果蝇算法[J].控制理论与应用,2015,32(4):540-545.(Zheng Xiaolong,Wang Ling. An order-based fruit fly optimization algorithm for sto-chastic resource-constrained project scheduling[J]. Control Theory&Applications,2015,32(4):540-545.)
[13]王晓伟,刘占生,窦唯.基于AR模型的声发射信号到达时间自动识别[J].振动与冲击,2009,28(11):79-83,204.(Wang Xiaowei,Liu Zhansheng,Dou Wei. Automatic arrival time identification ofacoustic emission signals based on AR model[J]. Journal of Vibra-tion and Shock,2009,28(11):79-83,204.)
[14]艾婷,张茹,刘建锋,等.三轴压缩煤岩破裂过程中声发射时空演化规律[J].煤炭学报,2011,36(12):2048-2057.(Ai Ting,Zhang Ru,Liu Jianfeng,et al. Space-time evolution rules of acousticemission locations under triaxial compression[J]. Journal of ChinaCoal Society,2011,36(12):2048-2057.)
[15]王恩元,何学秋,刘贞堂,等.煤体破裂声发射的频谱特征研究[J].煤炭学报,2004,29(3):289-292.(Wang Enyuan,HeXueqiu,Liu Zhentang,et al. Study on frequency spectrum characte-ristics of acoustic emission in coal or rock deformation and fracture[J]. Journal of China Coal Society,2004,29(3):289-292.)
[2]聂百胜,何学秋,王恩元,等.煤与瓦斯突出预测技术研究现状及发展趋势[J].中国安全科学学报,2003,13(6):40-46,83.(NieBaisheng,He Xueqiu,Wang Enyuan,et al. Present situation andprogress trend of prediction technology of coal and gas outburst[J].China Safety Science Journal,2003,13(6):40-46,83.)
[3]许江,耿加波,彭守建,等.不同含水率条件下煤与瓦斯突出的声发射特性[J].煤炭学报,2015,40(5):1047-1054.(Xu Jiang,Geng Jiabo,Peng Shoujian,et al. Acoustic emission characteristics ofcoal and gas outburst under different moisture contents[J]. Journal ofChina Coal Society,2015,40(5):1047-1054.)
[4]郭民臣,梅勇,马英,等.基于LabVIEW的声发射信号小波降噪方法研究[J].动力工程学报,2012,32(6):450-453.(GuoMinchen,Mei Yong,Ma Ying,et al. A wavelet method for denoi-sing of acoustic emission signal based on LabVIEW[J]. Journal ofChinese Society of Power Engineering,2012,32(6):450-453.)
[5]陈凯.基于经验模式分解的去噪方法[J].石油地球物理勘探,2009,44(5):603-608.(Chen Kai. A new denoising method basedon empirical mode decomposition(EMD)[J]. Oil GeophysicalProspecting,2009,44(5):603-608.)
[6]王向华,覃征,杨新宇,等.阈值去噪下的改进粒子滤波算法[J].西安交通大学学报,2010,44(2):31-34.(Wang Xianghua,QinZheng,Yang Xinyu,et al. Improved particle filter algorithm based onthreshold de-noising[J]. Journal of Xi’an Jiaotong University,2010,44(2):31-34.)
[7]向勇,李岩松.基于粒子滤波的光学电流互感器信号处理方法研究[J].电力系统保护与控制,2013,41(18):101-104.(XiangYong,Li Yansong. Research of signal processing of optical currenttransducer based on particle filter[J]. Power System Protectionand Control,2013,41(18):101-104.)
[8]段琢华,蔡自兴,于金霞.不完备多模型混合系统故障诊断的粒子滤波算法[J].自动化学报,2008,34(5):581-587.(Duan Zhuo-hua,Cai Zixing,Yu Jinxia. Particle filtering algorithm for fault diag-nosis of multiple model hybrid systems with incomplete models[J].Acta Automatica Sinica,2008,34(5):581-587.)
[9]王晶,宋策,杨立保.融合GrabCut的粒子滤波目标跟踪算法[J].仪器仪表学报,2014,35(S2):20-27.(Wang Jing,Song Ce,YangLibao. Grab Cut combined particle filter algorithm for tracking target[J]. Chinese Journal of Scientific Instrument,2014,35(S2):20-27.)
[10]胡士强,敬忠良.粒子滤波算法综述[J].控制与决策,2005,20(4):361-365,371.(Hu Shiqiang,Jing Zhongliang. Overview ofparticle filter algorithm[J]. Control and Decision,2005,20(4):361-365,371.)
[11]王林,吕盛祥,曾宇容.果蝇优化算法研究综述[J].控制与决策,2017,32(7):1153-1162.(Wang Lin,Lyu Shengxiang,Zeng Yu-rong. Literature survey of fruit fly optimization algorithm[J]. Controland Decision,2017,32(7):1153-1162.)
[12]郑晓龙,王凌.随机资源约束项目调度问题基于序的果蝇算法[J].控制理论与应用,2015,32(4):540-545.(Zheng Xiaolong,Wang Ling. An order-based fruit fly optimization algorithm for sto-chastic resource-constrained project scheduling[J]. Control Theory&Applications,2015,32(4):540-545.)
[13]王晓伟,刘占生,窦唯.基于AR模型的声发射信号到达时间自动识别[J].振动与冲击,2009,28(11):79-83,204.(Wang Xiaowei,Liu Zhansheng,Dou Wei. Automatic arrival time identification ofacoustic emission signals based on AR model[J]. Journal of Vibra-tion and Shock,2009,28(11):79-83,204.)
[14]艾婷,张茹,刘建锋,等.三轴压缩煤岩破裂过程中声发射时空演化规律[J].煤炭学报,2011,36(12):2048-2057.(Ai Ting,Zhang Ru,Liu Jianfeng,et al. Space-time evolution rules of acousticemission locations under triaxial compression[J]. Journal of ChinaCoal Society,2011,36(12):2048-2057.)
[15]王恩元,何学秋,刘贞堂,等.煤体破裂声发射的频谱特征研究[J].煤炭学报,2004,29(3):289-292.(Wang Enyuan,HeXueqiu,Liu Zhentang,et al. Study on frequency spectrum characte-ristics of acoustic emission in coal or rock deformation and fracture[J]. Journal of China Coal Society,2004,29(3):289-292.)
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