基于CEEMDAN和SVM的混凝土破坏状态声发射信号识别

针对混凝土破坏状态复杂多变、声发射(AE)信号难以从背景噪声中分离的问题，将自适应噪声完备集合经验模态分解(CEEMDAN)方法与支持向量机(SVM)方法耦合，对混凝土破坏状态声发射信号进行识别与预测。利用CEEMDAN方法对采集的声发射信号进行分解，获取一定数量的自适应特征模态(IMF)分量，并计算各分量与原声发射信号之间的相关系数，优选出包含原声发射信号主要频域信息的IMF分量。计算各分量的特征参数能量系数和波形系数，并将其分别输入SVM中对混凝土不同破坏状态进行分类识别，结果表明能量系数作为特征参数的预测率为92.39%,波形系数作为特征参数的预测率为91.30%。

Acoustic emission signal recognition of concrete failure state based on CEEMDAN and SVM

Aiming at the problem that the failure state of concrete is complex and changeable, and the acoustic emission(AE) signal is difficult to be separated from the background noise, the function of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was coupled with the support vector machine (SVM) method to identify and predict the concrete destructive AE signal. Firstly, the CEEMDAN method is used to decompose the acquired AE signal, obtaining a certain number of adaptive characteristic modal components (IMF). The correlation coefficient between each component and the original AE signal is calculated, and the IMF component containing more information about the original AE signal is preferred. Secondly, the energy coefficient and waveform coefficient of each component eigenvalue are calculated and inputted into the SVM respectively to classify and identify different failure states of concrete. The results show that the prediction rate of energy coefficient as eigenvalue is 92.39%, and the prediction rate of waveform coefficient as eigenvalue is 91.30%.