Abstract
Multifunctional sensor network has become a research focus in the field of structural health monitoring. To improve the reliability and stability of the diagnosis results, it is necessary to fuse heterogeneous signals under the interference of the external load and damage. In this paper, a piezoelectric-fiber hybrid sensor network is integrated to monitor the crack growth around the hole in the aviation aluminum plate. The effect of the load change on the signals of piezoelectric transducers (PZTs) and optical fiber sensors is analyzed. To improve the damage diagnosis result obtained by ultrasonic guided wave imaging diagnosis based on PZTs and strain damage identification based on distributed optical fiber sensor, a fusion strategy of heterogeneous signals based on a two-stage Kalman filtering algorithm is proposed. In the first stage, the features extracted from two types of sensors are fused at a specific time at the feature level, and then the location of the damage center is predicted. Then, the second fusion is to fuse the predicted damage location results at multiple specific times at the decision level. Crack growth monitoring experiments in hot spots of metallic material under bending moment loading is carried out to verify the feasibility of the proposed fusion method. The experimental results indicate that the fusion damage diagnosis results are more stable, moreover, the accuracy of damage location and quantification is improved than the single signal diagnosis results.
Highlights
In recent years, structural damage and failure have led to some dramatic accidents in the field of aeronautical and ocean engineering
This phenomenon indicates that the strain method based on optical fiber sensors has the shortcoming of monitoring area limitation
Guided wave imaging diagnosis based on piezoelectric transducers (PZTs) is prone to misjudgment due to baseline signal, and the monitoring area of fiber sensor monitoring is limited, only the signal along the fiber length direction can be monitored, etc
Summary
Structural damage and failure have led to some dramatic accidents in the field of aeronautical and ocean engineering. This phenomenon indicates that the strain method based on optical fiber sensors has the shortcoming of monitoring area limitation. The characteristics of the heterogeneous signal of PZTs and optical fiber sensors at time k were fused by Kalman filtering at the feature level, and predict the damage location which is known as the weighted least square method. The experimental results show that, compared with single-type PZTs and single-type optical fiber sensor monitoring, multi-source sensing information fusion based on the two-stage Kalman filtering can reduce the damage misjudgment and improve the damage monitoring accuracy. Multi-sensor data fusion can be further combined with artificial intelligence technology
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