Abstract
In order to avoid the false alarm and alarm failure caused by sensor malfunction or failure, it has been critical to diagnose the fault and analyze the failure of the sensor measuring system in major infrastructures. Based on the real time monitoring of bridges and the study on the correlation probability distribution between multisensors adopted in the fault diagnosis system, a clustering algorithm based onk-medoid is proposed, by dividing sensors of the same type intokclusters. Meanwhile, the value ofkis optimized by a specially designed evaluation function. Along with the further study of the correlation of sensors within the same cluster, this paper presents the definition and corresponding calculation algorithm of the sensor’s validation. The algorithm is applied to the analysis of the sensor data from an actual health monitoring system. The result reveals that the algorithm can not only accurately measure the failure degree and orientate the malfunction in time domain but also quantitatively evaluate the performance of sensors and eliminate error of diagnosis caused by the failure of the reference sensor.
Highlights
In recent years, with a series of catastrophic accidents of infrastructure damage and collapse, the research of infrastructure structural health monitoring and early warning technology for the public safety has been promoted [1,2,3]
The results show that the abnormal sensor can be found in multiple sensors and the fault can be located in time domain
Based on the correlation of the data collected by sensors, the sensors in the bridge monitoring system can be divided into multiple sensor clusters through k-medoid clustering algorithm
Summary
With a series of catastrophic accidents of infrastructure damage and collapse, the research of infrastructure structural health monitoring and early warning technology for the public safety has been promoted [1,2,3]. The structural complexity of infrastructure and the diversity of influential factors lead to typical application of multisensor systems in these monitoring systems As such large permanent infrastructures, like bridge, dam, and nuclear power plant, are designed with a service life of decades, a severe challenge to the sensor system with service life of only a few years is posed. A faulty sensor cannot perform its function properly but instead may provide false information for evaluation, leading the system to produce the wrong diagnosis. By analyzing the diversity of the redundant information, the sensor fault is identified [9] This approach has been widely employed in the critical equipment such as Mathematical Problems in Engineering. How to identify the sensor fault by recognizing and utilizing the correlation between the data acquired by the different sensors becomes a valuable research field [11, 12]. In the case that the reference sensor has failure or fault, the failure components can be eliminated, effectively ensuring the robustness of algorithm
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