Application of Noise Cancelling and Damage Detection Algorithms in NDE of Concrete Bridge Decks Using Impact Signals
Application of Noise Cancelling and Damage Detection Algorithms in NDE of Concrete Bridge Decks Using Impact Signals
复制标题
噪声消除和损伤检测算法在使用冲击信号的混凝土桥面无损检测中的应用
DOI：
10.1007/s10921-011-0114-8
复制
发表时间：
2011
影响因子：
2.8
通讯作者：
P. Ramuhalli
中科院分区：
文献类型：
--
作者：
Gang Zhang;R. Harichandran;P. Ramuhalli
脱层是混凝土桥面常见的病害。在所有的分层检测方法中，声学方法具有快速和廉价的优点。在传统的声学检查方法中，检查员单独拖动链条或在桥面上锤击，并从声音的“空洞”中检测分层。信号往往被周围的交通噪声污染，分层的检测是高度主观的。本文介绍了一种基于碰撞的声学无损检测方法的性能，该方法采用噪声抵消算法过滤交通噪声，并通过引入特征提取和模式识别算法消除主观性。对不同的算法进行了比较，并在每个类别中选择了最好的一个。比较结果表明，改进的独立分量分析（伊卡）算法在消除交通噪声方面效果最好，由梅尔倒谱系数（MFCC）组成的特征在可重复性和可分性方面表现最好。然后，通过径向基函数（RBF）神经网络检测桥面的状态。该系统的性能进行了评估，使用实验和现场数据。结果表明，所选算法提高了声学方法的噪声鲁棒性，并且在训练数据具有代表性的情况下表现令人满意。
Delamination is a commonly observed distress in concrete bridge decks. Among all the delamination detection methods, acoustic methods have the advantages of being fast and inexpensive. In traditional acoustic inspection methods, the inspector drags a chain alone or hammers on the bridge deck and detects delamination from the “hollowness” of the sound. The signals are often contaminated by ambient traffic noise and the detection of delamination is highly subjective. This paper describes the performance of an impact-based acoustic NDE method where the traffic noise was filtered by employing a noise cancelling algorithm and where subjectivity was eliminated by introducing feature extraction and pattern recognition algorithms. Different algorithms were compared and the best one was selected in each category. The comparison showed that the modified independent component analysis (ICA) algorithm was most effective in cancelling the traffic noise and features consisting of mel-frequency cepstral coefficients (MFCCs) had the best performance in terms of repeatability and separability. The condition of the bridge deck was then detected by a radial basis function (RBF) neural network. The performance of the system was evaluated using both experimental and field data. The results show that the selected algorithms increase the noise robustness of acoustic methods and perform satisfactorily if the training data is representative.
DOI：
10.1109/tsa.2005.858005
发表时间：
2006-07-01
影响因子：
--
作者：
Vincent, Emmanuel;Gribonval, Remi;Févotte, Cedric
通讯作者：
Févotte, Cedric