基于VMD-Seq2seq 模型的扬声器短期寿命预测研究
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TN912

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Prediction of speaker residual life based on VMD-Seq2seq model
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    摘要:

    随着音频载体设备的发展,扬声器在向着体积小、功率大的趋势发展,长时间工作在大信号驱动时音圈发热严重,会 出现音圈断路等热损坏问题,通过对扬声器电参量数据的预测,可以降低功放功率等方法保护音圈,延长使用寿命。针对功 率试验中扬声器电参量数据的时序特性,提高预测准确率,提出一种基于变分模态分解(variational mode decomposition, VMD)和编解码器(sequence to sequence,Seq2seq)模型的扬声器电参量多步预测方法。该方法首先使用VMD将原始数据进 行分解,降低数据的非平稳性,利用分解后的数据构建训练集并使用Seq2seq网络模型进行训练和多步预测。仿真结果表明, 所提出的预测模型在单步预测情况下,模型评价指标均方根误差(RMSE)为0.044、平均绝对百分比误差(MAPE)为0.15%、 决定系数(R²) 为0.94,在五步预测的情况下,模型评价指标RMSE为0.05、MAPE为0.17%、R² 为0.92,均优于其余对比模 型,表明所提出模型的精度更高。

    Abstract:

    With the development of audio carrier equipment,the loudspeaker in the trend of small volume,large power development,long time working in large signal drive when the voice coil heating serious,there will be a voice coil break and other thermal damage problems,through the prediction of the loudspeaker electrical parameters data,can reduce the power amplifier power and other methods to protect the voice coil,prolong the service life.Aiming at the time sequence characteristics of electrical parameter data of loudspeaker in power test,the prediction accuracy is improved.A multi-step prediction method for loudspeaker electrical parameters is proposed based on variational mode decomposition(VMD)and sequence to sequence(Seq2seq)model.First,VMD isused to decompose the original data to reduce the non-stationarity of the data.Then,the decomposed data is used to build a training set and the Seq2seq network model is used for training and multi-step prediction.The simulation results show that in the case of single step prediction,the model evaluation index RMSE is 0.044,MAPE is 0.15%,R²is 0.94,and in the case of five-step prediction,the model evaluation index RMSE is 0.05,MAPE is 0.17%,R²is 0.92,allofwhich are better than other comparison models.It shows that the accuracy of the proposed model is higher

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李天宇,周静雷,李佳斌.基于VMD-Seq2seq 模型的扬声器短期寿命预测研究[J].国外电子测量技术,2023,42(3):145-151

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  • 在线发布日期: 2024-10-22
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