Intelligent Diagnosis Techniques For Structural Health Condition Using Support Vectors

Damage diagnosis is the precondition of structural health monitoring (SHM). For the purpose of structure health monitoring and diagnosis, intelligent damage diagnosis approaches based on the wavelet packet analysis and support vectors (SVs) are studied in this paper.The necessity of civil engineering SHM is firstly discussed. Secondly, the concept of SHM & damage diagnosis, and the architecture of SHM system are introduced. Then, structure damage diagnosis techniques and their development are reviewed. In order to extract damage features from noisy signal, the principle of multi-resolution analysis and orthogonality of wavelet packet transform (WPT) are investigated. Experimental results shown: (1)The wavelet packet energy distribution can indicate the component energy variation in a signal. (2) The bigger supported intervals an orthogonal wavelet has, the better orthogonality the wavelet possesses. It is helpful to isolate the components into different sub frequencybands and have less redundancy in these frequencybands. (3) Signals form different kinds of damage reveal different packet energy distribution through WPT, and for a special damage the wavelet packet energy distribution is different at different measurement nodes. (4)Demage features embedded in structure response signal can be distinctly clarified with the wavelet packet energy distribution which is robust to noise. It can be used as an ideal feature index to represent the structural health condition.In order to solve problems of faulty sample shortage and proceesed data overabundance in damage diagnosis, an intelligent method using support vector machine (SVM) is proposed by means of extracting feature with WPT. According to the method, the energy sequences of different frequency bands decomposed by WPT are investigated, which are input to a multi-classified support vector machines to implement multi-damage recognition and damage localization. The classification accuracy of the proposed method is greatly improved compared with the multi-classification SVM without feature extraction. In order to enhance the accuracy and robustness of the system decision-making, and avoid the information unintegrity of only using the sole signal in damage diagnosis, another SVM diagnosis method based on the data fusion technique is put forward. Constructing damage feature vectors with extracted features from several measurement nodes using WPT, and inputing these feature vectors to a SVM classifier, degree and localization of damages are found. The diagnosis information was enriched by means of data fusion, and the uncertainty of damage detection information was also depressed. Since diagnosis information from the measurement nodes is redundant and supplementary, the diagnosis accuracy is greatly improved.Because it is difficult to obtain the damage samples in engineering application, and for the purpose of on-line automatic monitoring and diagnosis for structural health condition, a new monitoring and diagnostics method based on support vector data description (SVDD) is proposed, which only needs samples under normal condition, and needs no abnormal samples. WPT is also used to preprocess original signals, the signal energies in different frequency-bands are taken as condition feature. Then the features from different measurement nodes are fused as a target vector. A developed SVDD classifier is applied to implement structural condition monitoring by inputting the target vector. SVDD classifier was able to distinguish the normal and abnormal condition of structure ideally, and can be used as an automation approach for monitoring and diagnosis.