多重分形去趋势波动分析的振动信号故障诊断
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摘要
针对基于配分函数的多重分形分析不利于局部标度特性突显的问题,把多重分形去趋势波动分析(MF-DFA)方法引入到振动诊断领域,提出对振动信号进行多重分形谱参数(|B|,α0,Δα和Δf)故障特征分析,并将α0用于故障诊断.首先分析了振动信号的多重分形特性;然后提取振动信号的4种多重分形谱参数特征,并进行了比较;最后用支持向量机算法实现振动故障诊断.研究表明:去除趋势后,振动信号的波动呈现显著多重分形特征,正常状态振动信号的α0明显大于故障状态,而振动信号的|B|,Δα和Δf特征变化规律则不明显;α0作为故障特征量,能有效地区分正常状态与故障状态,有效实现了振动故障诊断.
As partition function-based multiracial analysis is not easy to highlight the local scaling properties,the multi-fractal detrended fluctuation analysis(MF-DFA) was used to diagnose vibration faults,in which the vibration signals analysis was based on the features of the parameters(|B|,α0,Δα and Δf) of multi-fractal spectrum and α0 was employed to fault diagnosis.First,the multifractal characteristics of the vibration signal were analyzed.Then four kinds of multifractal spectrum parameters characteristics of the vibration signals were extracted and compared with each other.Finally,a support vector machine was applied to fault diagnosis in the vibration signals.Simulation results prove that the fluctuation of the vibration signals show significant multi-fractal characteristics and the α0 of the vibration signal in a normal state is significantly higher than an abnormal stats.However,the |B|,Δα and Δf features of the vibration signal show no significant differences.These demonstrate that the parameters α0 of singular spectrum as a fault feature value can distinguish between normal status and fault status with high performance for the vibration fault diagnosis.
As partition function-based multiracial analysis is not easy to highlight the local scaling properties,the multi-fractal detrended fluctuation analysis(MF-DFA) was used to diagnose vibration faults,in which the vibration signals analysis was based on the features of the parameters(|B|,α0,Δα and Δf) of multi-fractal spectrum and α0 was employed to fault diagnosis.First,the multifractal characteristics of the vibration signal were analyzed.Then four kinds of multifractal spectrum parameters characteristics of the vibration signals were extracted and compared with each other.Finally,a support vector machine was applied to fault diagnosis in the vibration signals.Simulation results prove that the fluctuation of the vibration signals show significant multi-fractal characteristics and the α0 of the vibration signal in a normal state is significantly higher than an abnormal stats.However,the |B|,Δα and Δf features of the vibration signal show no significant differences.These demonstrate that the parameters α0 of singular spectrum as a fault feature value can distinguish between normal status and fault status with high performance for the vibration fault diagnosis.
引文
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[13]苑莹,庄新田,金秀.期货价格收益序列的多重分形统计描述及成因分析[J].东北大学学报:自然科学版,2010,31(4):605-608.
[14]Turie E,Tadeo J L,Martin E.Molecularly imprin-ted polymeric fibers for solid phase microextraction[J].Anal Chem,2007,79(8):3099-3104.
[15]Lane M R B,Rocha,Benjamim R D,et al.SVM practical industrial application for mechanical faults diagnostic[J].Expert Systems with Applications,2011,38(6):6980-6984.
[2]钟明寿,龙源,谢全明,等.基于分形盒维数和多重分形的爆破地震波信号分析[J].振动与冲击,2010,29(1):7-12.
[3]王金东,王巍,李宏灿.往复压缩机轴承故障的多重分形特征提取[J].振动与冲击,2008,27(S):313-315.
[4]Li M,Ma W X,Liu X J.Investigation of rolling bear-ing fault diagnosis based on multi-fractal and general fractal dimension[C]∥Second Intelligent Computa-tion Technology and Automation.Zhangjiajie:IEEE Computer Society,2009:545-548.
[5]Yu Y,Li B L,Shang J S,et al.The application of vibration signal multi-fractal in fault diagnosis[C]∥Second International Conference on Future Networks.Sanya:IEEE Computer Society,2010:164-167.
[6]Tang J Y,Shi Y B,Zhou L F,et al.Nonlinear ana-log circuit fault diagnosis using wavelet leaders multi-fractal analysis method[J].Control and Decision,2010,25(4):605-609.
[7]Halsey T C,Jensen M H,Kadanofflp,et al.Fractal measures and their singularities:the characterization of strange sets[J].Physical Review A:1986,33(2):1141-1151.
[8]Peng C K,Havlin S,Stanley H E,et al.Quantifica-tion of scaling exponents and crossover phenomena in nonstationary heartbeat time series[J].Chaos,1995,5(1):82-87.
[9]Kantelhardt J W,Zschiegner S A,Braun P,et al.Multifratal detrended fluctuation analysis of nonsta-tionary time series[J].Phsica A:2002,316(87):87-114.
[10]肖毅,周前祥,茆明,等.基于FA奇异测度的多重分形维分析[J].系统工程与电子技术,2009,31(4):964-967.
[11]罗世华,曾九孙.基于多分辨分析的高炉铁水含硅量波动多重分形辨识[J].物理学报,2009,58(1):150-157.
[12]孙斌,许明飞,周云龙.气液两相流波动信号多重分形去趋势波动分析[J].工程热物理学报,2011,32(5):795-798.
[13]苑莹,庄新田,金秀.期货价格收益序列的多重分形统计描述及成因分析[J].东北大学学报:自然科学版,2010,31(4):605-608.
[14]Turie E,Tadeo J L,Martin E.Molecularly imprin-ted polymeric fibers for solid phase microextraction[J].Anal Chem,2007,79(8):3099-3104.
[15]Lane M R B,Rocha,Benjamim R D,et al.SVM practical industrial application for mechanical faults diagnostic[J].Expert Systems with Applications,2011,38(6):6980-6984.
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