Damage Detection Based On Sensor Performance Degradation Considered

Structural health monitoring is implemented by assessment the structural health state via the response change of a structure. In current damage detection methods, only the effect of loading and environment on damage is considered for the measured responses. Due to the fact that the sensor is deteriorated and is even in fault with the variation of time and environment, this seriously influences the diagnosis ratio and increases the detection difficulty. Therefore, it is crucial to distinguish the structural responses caused by environment and sensor performance degradation from the measured responses to improve the damage detection accuracy.In order to overcome the above-mentioned problems, series of studies and achievements are developed in this thesis.(1) The response patterns in quality control charts caused by sensor performance degradation and structural damage are developed. It is verified that structural damage and sensor performance degradation both cause abnormal responses by development on the algorithm for detecting abnormal structural response and isolating degradative sensors. In the light of the fact that structural damage impacts global structural response and sensor performance degradation impacts on the global structural response, it is effectively implemented to distinguish the source of abnormal structural responses. That is to say, the response pattern in quality control chart changes when sensors degrade, while response pattern in quality control chart remain unchanged when structure damages. Via numerical experiment and laboratory model test, it is found that the response pattern in quality control chart caused by sensor performance degradation is nonstationary, while the response pattern in quality control chart caused by structural damage is staionary.(2) This thesis proposes an improved algorithm for detecting abnormal structural response and isolating degradative sensors. The improved indicator of quality control chart for detecting sensor performance degradation is sensitive to structural damage but not sensitive to sensor performance degradation. In a word, the indicator value caused by the sensor performance degradation exceeds the limits in quality control chart, while this case doesn’t occur in structural damage. Therefore it is effective and easy to distinguish the source of abnormal response by quality control chart. The improved algorithm simplifies the sensor fault identification process and persuasive.(3) A new two-stage damage detection method is proposed in this thesis, which is integrated by modal parameters and the multigroup cooperation particle swarm algorithm (MCPSO). In the first stage, it is used to localize the damage based on modal strain energy. This is proved effective and feasible; furthermore, it is in favor for determining the degree of damage in the next stage. In the second stage, an appropriate threshold is firstly determined to filter the units with little damage (as no damage units) and encode the units with damage. Then MCPSO is applied to determine damage units and detect the extent of damage precisely via the iterative optimization, which it is verified accurate and efficient for damage identification.