基于KPCA和TCN-Attention的滚动轴承退化趋势预测
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1.昆明理工大学 信息工程与自动化学院 昆明 650500 2.云南省人工智能重点实验室 昆明 650500

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TH17;TN911

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国家自然科学基金(51765022)


Prediction of rolling bearing degradation trend based on KPCA and TCN-Attention
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1. Faculty of Information Engineering and automation,Kunming University of Science and Technology,Kunming 650500,China; 2. Key Laboratory of Artificial Intelligence of Yunnan Province,Kunming 650500,China

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    摘要:

    为准确地对滚动轴承退化趋势进行预测,提出了KPCA和TCN-attention的组合预测方法。首先,利用KPCA对轴承的高维特征集进行非线性特征提取,并将第一主成分作为轴承的性能退化指标,对第一主成分进行归一化和平滑预处理;然后,在时间卷积网络TCN中加入注意力机制来赋予隐藏层中关键特征的权重系数,找出TCN提取每个时间步的局部特征中贡献最大的部分,进而筛选出关键信息;最后,利用辛辛那提IMS轴承外圈和内圈的全生命周期数据对所提方法的可行性进行了验证,实验结果表明,与未加注意力机制的TCN和GRU、LSTM对比,所提方法的外圈RMSE和MAE预测指标分别降低至0.00299和0.00217,内圈RMSE和MAE预测指标分别降低至0.03401和0.02490,具有更高地预测准确性。

    Abstract:

    In order to accurately predict rolling bearing degradation trend, a combined prediction method of KPCA and TCN-Attention was proposed. Firstly, KPCA was used to extract nonlinear features from the high-dimensional feature sets of bearings, and the first principal component was used as the performance degradation index of bearings to normalize and smooth the first principal component. then, an attention mechanism is added to the temporal convolutional network TCN, the weight coefficients of the key features of the hidden layer are given, the part that contributes the most to the local features extracted by the TCN at each time step is found, and then the key information is extracted; finally, the Cincinnati IMS is used. The life cycle data of the bearing outer and inner rings verify the feasibility of the method. The experimental results show that compared with TCN, Gru and LSTM without attention mechanism, the predicted values of RMSE and MAE of the outer loop are reduced to 0.00299 and 0.00217, respectively, and the predicted values of RMSE and MAE of the inner loop are reduced to 0.03401 and 0.02490, respectively , with higher prediction accuracy.

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严帅,熊新.基于KPCA和TCN-Attention的滚动轴承退化趋势预测[J].电子测量技术,2022,45(15):28-34

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  • 在线发布日期: 2024-04-08
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