Statistics-Based Noise Analysis for Vibration-Based Damage Identification

As the vibration-based structural damage detection methods are easily affected by the environmental noise, a novel noise analysis method is proposed based on the statistics in this paper together with the Monte Carlo technique for assessing the influence of experimental noise of modal data on sensitivity-based damage detection methods. Different from the commonly used random perturbation technique, the proposed technique is deduced directly by the Moore–Penrose generalized inverse of sensitivity matrix under the differential quotient rule of composite function. It can not only make the analysis process more effective but also analyze the noise influence on both frequencies and mode shapes in a similar way. Furthermore, an improved modal sensitivity based damage detection method is also proposed and compared with other two commonly used sensitivity-based methods in this paper. A one-story portal frame is adopted to evaluate the efficiency of both the proposed noise analysis technique and the improved modal sensitivity based method. The assessment results show that the proposed statistics-based noise analysis technique is effective and more suitable for the vibration-based damage identification. The improved modal sensitivity based method is more robust to noise than the other commonly used sensitivity-based methods.