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基于QPSO-SVR和声发射信号的机械密封寿命预测
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中央高校基本科研业务费专项资金项目(2682016CX033).


Prediction of Mechanical Seal Life Based on QPSO-SVR and Acoustic Emission Signal
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    摘要:

    针对现有机械密封监测技术难以有效预测剩余使用寿命的问题,提出基于声发射特征融合的退化指标和QPSO-SVR寿命预测模型的机械密封剩余寿命预测方法。首先通过实验采集多组机械密封的全寿命数据,进行小波阈值降噪处理,从原始声发射信号中提取出能表征机械密封运行状态的特征,利用KPCA分析优化得到的声发射特征,然后通过马氏距离对得到的特征进行融合进而得到能够表征机械密封退化的指标,利用QPSO优化SVR模型参数,建立寿命预测模型。实验结果显示:基于退化指标和QPSO-SVR模型的寿命预测方法有着较好的泛化能力和较高的精度,具有良好的工业前景。

    Abstract:

    Mechanical seal life is an important index for the operation of large rotating equipment in process industries such as petrochemical industry.Aimed at the deficiencies of existing study of mechanical seal in prediction of using life,and the unknown of regression model,a new prediction method of mechanical seal was proposed based on extracting features from AE signal and support vector regression.Several groups of full life data in mechanical seal were acquired in experiment and were processed by wavelet denoising,the features in time domain and frequency domain representing the mechanical seal operation state were extracted,the mechanical seal degradation index was acquired by treating the highdimensional features with KPCA and Mahalanobis distance,and a prediction model was got by inputting the index into QPSO to estimate SVR parameters.The experiment result of application shows that life predication method based on AE signal features and QPSO-SVR model has strong generalization and high prediction precision.

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胡龙飞,傅攀,林志斌,张思聪,赵蕾.基于QPSO-SVR和声发射信号的机械密封寿命预测[J].润滑与密封,2019,44(4):40-45.
. Prediction of Mechanical Seal Life Based on QPSO-SVR and Acoustic Emission Signal[J]. Lubrication Engineering,2019,44(4):40-45.

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  • 在线发布日期: 2019-07-04
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