Using a single sensor for bridge condition monitoring via moving embedded principal component analysis

This article presents a novel data-driven structural damage detection method named moving embedded principal component analysis to monitor the bridge condition and detect the damage occurrence using only one sensor. A fixed moving window is used to cut out the time series of the recorded data for the analysis. The data set inside the window is embedded to be a multidimensional state space using time delay method. The matrix of the state space is analyzed using the standard principal component analysis method, and a novel damage index R-j defined with the eigenvalue is proposed to identify structural damage occurrence. The window length is determined by a new approach through examining the convergent spectrum of the contribution ratio of the first principal component of the embedded state space. The time delay is determined by the autocorrelation function of the response, and the embedding dimension is obtained by the cumulative contribution ratio of the state space. The windowed damage index can be calculated continuously by moving the window along the recorded vibration data. To demonstrate the performance of the proposed method, responses of a beam bridge model subjected to stochastic loads obtained with numerical simulations and experimental tests are analyzed to monitor the structural conditions. The results demonstrate that the proposed method can accurately identify the occurrence of damage and the abnormal behavior of the structure. The recorded data on a large suspension bridge are also analyzed. The analysis successfully identified an incident on this bridge when it was slightly scraped by the mast of a sand ship. This further verifies the effectiveness of the proposed method.