多工况下基于EEMD-ICNN的输油管道泄漏识别

针对多工况下管道泄漏信号预处理繁琐、误报率高的问题，提出了一种集合经验模态分解(EEMD)结合改进卷积神经网络(ICNN)的泄漏识别模型。所用识别方法采用EEMD将泄漏信号分解成若干个具有稳态性能的固有模态分量(IMF),通过相关系数划分出噪声主导向量并予以去除实现信号重构；提取重构信号的一系列指标特征作为ICNN模型的输入进行特征提取，实现管道多工况分类；ICNN在每个卷积层和池化层之间加入批量归一化层，以此加快网络训练速度。结果表明：所提模型能够快速准确识别出停泵、调阀、泄漏、正常工况，且在较少训练数据下平均识别准确率可达98.25%。与未改进的CNN和SVM分类识别模型相比，该方法有效提高了识别准确率。

Oil pipeline leakage identification based on EEMD-ICNN under multiple working conditions

Aiming at the problems of cumbersome pre-processing and high false alarm rate of pipeline leakage signals under multiple working conditions,an ensemble empirical mode decomposition (EEMD) combined with improved convolutional neural network (ICNN) is proposed as a leakage identification model. The proposed identification method uses EEMD to decompose the leak signal into several intrinsic modal components (IMFs) with steady-state performance, and the noise dominant vectors are divided and removed by correlation coefficients to achieve signal reconstruction. A series of indicator features of the reconstructed signal are extracted as the input of the ICNN mode for feature extraction to achieve the multi-condition classification of pipeline. The batch normalization layer was added by ICNN between each convolutional layer and the pooling layer to accelerate the network training. In accordance with the results, it indicates that the proposed model can quickly and accurately identify pump shutdown, valve adjustment, leakage and normal operating conditions, which can reach 98.25% of the average recognition accuracy under less training data.This technique significantly raises the accuracy of recognition when compared to the unimproved CNN and SVM classification recognition models.