Sensor fault detection with generalized likelihood ratio and correlation coefficient for bridge SHM

Data is obtained from sensors in a structural health monitoring system for integrity assessment of the structure, and false alarm will be frequently triggered if a faulty sensor is present. A method based on the generalized likelihood ratio and correlation coefficient is presented to identify senor fault in this study. The acceleration response of a bridge is assumed Gaussian distributed when under operational condition, and evaluation of each sensor in the sensor network is accomplished via the minimum mean-squares-error algorithm. Multiple hypothesis test with the generalized likelihood ratio is then applied to the measured data with estimation to detect the sensor fault. Five common sensor fault types are studied with two correlation coefficients calculated between the estimation and measured data as the classification features. Unbalanced binary tree method is implemented to categorize the type of sensor fault. Numerical and experimental studies indicate that the proposed method is robust in the detection and classification of the sensor fault.