Ultrasonic sensing, for instance for damage or fouling detection, is commonly carried out using rigid transducer collars, carefully placed for monitoring a well-defined local area of a structure. A distributed sensing network consisting of individually placed transducers offers significant opportunities for monitoring larger areas or more complex geometries. For analyzing the signals of such a distributed system, we inherently require precise information on the sensor locations, the physical characteristics of the sensed medium, and the quality of the transducer coupling. Determining these parameters with sufficient accuracy is time-consuming even in laboratory conditions. More importantly, these parameters often change over time in industrial setups due to maintenance operations, the gradual degradation of the coupling, or a change in material characteristics as a result of deformations or fouling accumulation. We propose an automatic data-driven approach for overcoming this challenge. We infer accurate sensor locations and physical characteristics of the sensed medium by aligning observed signal features with a physical forward simulation, providing an automatic routine for both the initial estimation of the required parameters as well as their later automatic adaptation to compensate for drifts during operations. The method is successfully demonstrated in two separate ultrasonic sensing configurations, without requiring prior knowledge of the structure material or accurate sensor locations.

中文翻译：

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超声波传感网络的自适应


超声波传感，例如用于损坏或污垢检测，通常使用刚性传感器环进行，这些传感器环经过精心放置，用于监测结构的明确局部区域。由单独放置的传感器组成的分布式传感网络为监测更大区域或更复杂的几何形状提供了重要的机会。为了分析这种分布式系统的信号，我们本质上需要有关传感器位置的精确信息、感测介质的物理特性以及传感器耦合的质量。即使在实验室条件下，以足够的精度确定这些参数也很耗时。更重要的是，在工业设置中，由于维护操作、联轴器的逐渐退化或由于变形或污垢积累而导致的材料特性变化，这些参数通常会随着时间的推移而变化。我们提出了一种自动数据驱动的方法来克服这一挑战。我们通过将观察到的信号特征与物理正向模拟对齐来推断传感器的准确位置和传感介质的物理特性，为所需参数的初始估计以及以后的自动适应提供自动例程以补偿操作过程中的漂移。该方法在两种独立的超声波传感配置中成功演示，无需事先了解结构材料或准确的传感器位置。