基于优化VMD和连续隐马尔科夫模型的管道堵塞状态评估

伍林峰1,2,冯早1,2,朱雪峰1,2

振动与冲击 ›› 2020, Vol. 39 ›› Issue (22) : 214-222.

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PDF(2258 KB)
振动与冲击 ›› 2020, Vol. 39 ›› Issue (22) : 214-222.
论文

基于优化VMD和连续隐马尔科夫模型的管道堵塞状态评估

  • 伍林峰1,2,冯早1,2,朱雪峰1,2
作者信息 +

Pipeline blocking state assessment based on optimized VMD and continuous hidden Markov model

  • WU Linfeng1,2, FENG Zao1,2, ZHU Xuefeng1,2
Author information +
文章历史 +

摘要

本文面向U型管堵塞状态演变过程中故障程度的评估问题,提出一种基于低频声压信号分析和连续隐马尔科夫模型(Continuous hidden Markov model, CHMM)的U型管堵塞状态评估方法。该方法首先利用声波作为激励来观测U型管沉积物的堆积程度,对不同堵塞状态下的低频声压信号进行变分模态分解(Variational mode decomposition, VMD),根据分量幅值谱图确定变分模态分解的最佳模态分解数 并通过声压级变换筛选有效的IMF分量;然后提取有效IMF(Intrinsic Mode Function,IMF)分量的多尺度熵(Multi-scale entropy, MSE)特征,构建反映U型管不同程度堵塞状态的特征向量,最后将特征向量用于CHMM模型训练,建立能对U型管堵塞状态进行评估的模型。通过对U型管不同程度堵塞状态的试验数据进行测试,评估结果表明:该模型能准确评估U型管堵塞状态的程度变化,具有一定的工程应用价值。

Abstract

Aiming at assessing the degree of fault in the dynamic evolution of the U-tube blocking status, an evaluation method based on the low-frequency sound pressure signal analysis and continuous hidden Markov model (CHMM) was proposed.First, acoustic waves as excitation were used to observe the characteristics of sediment in the U-tube.The low-frequency sound pressure signals were decomposed by the variational mode decomposition (VMD), the optimal mode decomposition number k of the VMD was determined according to the component amplitude spectrum and the effective intrinsic mode function (IMF) components were screened out by sound pressure level transformation.Then the multi-scale entropy of the IMF components were extracted so as to construct the feature vectors that can effectively represent the signals.Finally, the feature vectors were used in the CHMM model training and a model for evaluating the blocking degree of the U-tube was established.The evaluation results show that the blocking status of the U-tube can be evaluated effectively by the model proposed, which has certain engineering application value.

关键词

U型管 / 声压信号 / 变分模态分解 / 堵塞状态 / CHMM模型

Key words

U-tube / sound pressure signal / variational mode decomposition(VMD) / blockage state continuous hidden Markov model(CHMM)

引用本文

导出引用
伍林峰1,2,冯早1,2,朱雪峰1,2. 基于优化VMD和连续隐马尔科夫模型的管道堵塞状态评估[J]. 振动与冲击, 2020, 39(22): 214-222
WU Linfeng1,2, FENG Zao1,2, ZHU Xuefeng1,2. Pipeline blocking state assessment based on optimized VMD and continuous hidden Markov model[J]. Journal of Vibration and Shock, 2020, 39(22): 214-222

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