Study Of Structural Signal Processing And Damage Identification Based On PCA And ICA

The structure health monitoring is a comprehensive technology and has become one of the most active reseach areas. Structure signal processing and damage identification are the mainly two major parts of health monitori system.The structure condition is monitored and assessed by sensor signals, namely the characteristic indexes are extracted from the sensor signals to monitor and diagnose structure condition. Because many disturbance factors exist in the sensor signals, the effective and reliable signal analysis processing method is proposed to enhance the reliability and accuracy of data, so as to better indentify the structure damage condition and localization. The key problem of structure damage identification is to find out the characteristic indexes which are enough sensitive to the change of structure system parameter. Therefore, signal processing and damage identification on structure sensor signals has the academic value and practical significance.The principal components analysis (PCA) reflects the second-order statistical characteristic of structure property and can transform the structure signals from the high dimension space to the low dimension characteristic space. The independent component analysis (ICA) reflects the higher order statistical characteristic of structure property. Correspondingly to PCA, ICA gets rid of the correlation of the components; moreover require the components statistical independent. No matter PCA or ICA, they all reflect the essential characteristic between structure conditions in statistical significance.The method of PCA and ICA are introduced into signal processing and damage identification of civil engineering structure. The main research work of this article is followed:1. Study of structural signal processing based on PCA and ICA(1) Based on the PCA theory, the relativity between structure signals is eliminated by their second-order statistical characteristic. The low dimension characteristic of PCA represent the property of sensor signals, thus the dimensionality of structure signals is reduced. The dimensionality reduction processing of vibration experimental signals indicates that the minority components have contained the main characteristic of structure signals.(2) The noise origin and its characteristic in frequency domain is analyzed, the anti-noise technology is presented. The frequency spectrum and the statistical property of noise are different from the vibration signal so as to differentiate them. These characteristics are illustrated through the numerical example and the vibration experiment signal analysis. (3) The noise reduction algorithm based on ICA is proposed to structure vibration signal processing. Because of the independence characteristic of the noise and structural vibration signals, the noise can be partly or completely separated from the sensor signals with ICA algorithm. At the same time, the article proposes two methods of sensor collocation. Noise is reduced with increasing the expansion channel of the same sensor point and the addition noise channel. The data processing of vibration experiment validates the feasibility and validity of the two methods.2. Study of structure signal processing based on PCA and ICA(1) Introduces the correlation coefficient to measure the comparability between the two structure conditions, and the damage characteristic extraction indexes based on PCA and ICA are constructed. The two extraction indexes can reflect the statistical characteristic of structure condition.(2) In order to reduce the computation quantity of the data and extract the damage characteristic, PCA damage characteristic indexes is constructed to reduce data dimensionality. The number of principal component is confirmed by the accumulative contribution ratio of PCA. The feasibility of PCA characteristic index is validated through the structure vibration experiment data.(3) The statistical characteristic of ICA benchmark filter between different condition structure vibration signals is discussed in the article. The damage characteristic index basis of ICA is constructed to identify the structure damage. The influence of different benchmark condition to damage identification has analyzed through the vibration experiment.(4) The method of structure district damage examination is proposed to recognize the damage state and position.