State-of-the-art ultrasonic non-destructive evaluation (NDE) uses an array to rapidly generate multiple, information-rich views at each test position on a safety-critical component. However, the information for detecting potential defects is dispersed across views, and a typical inspection may involve thousands of test positions. Interpretation requires painstaking analysis by a skilled operator. In this paper, various methods for fusing multi-view data are developed. Compared with any one single view, all methods are shown to yield significant performance gains, which may be related to the general and edge cases for NDE. In the general case, a defect is clearly detectable in at least one individual view, but the view(s) depends on the defect location and orientation. Here, the performance gain from data fusion is mainly the result of the selective use of information from the most appropriate view(s) and fusion provides a means to substantially reduce operator burden. The edge cases are defects that cannot be reliably detected in any one individual view without false alarms. Here, certain fusion methods are shown to enable detection with reduced false alarms. In this context, fusion allows NDE capability to be extended with potential implications for the design and operation of engineering assets.

中文翻译：

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融合多视图超声数据以提高无损评估中的检测性能

最先进的超声波无损评估 (NDE) 使用阵列在安全关键组件的每个测试位置快速生成多个信息丰富的视图。然而，用于检测潜在缺陷的信息分散在各个视图中，典型的检查可能涉及数千个测试位置。口译需要熟练的操作人员进行细致的分析。在本文中，开发了融合多视图数据的各种方法。与任何一种单一视图相比，所有方法都显示出显着的性能提升，这可能与 NDE 的一般和边缘情况有关。在一般情况下，在至少一个单独的视图中可以清楚地检测到缺陷，但视图取决于缺陷的位置和方向。这里，数据融合的性能提升主要是从最合适的视图中选择性使用信息的结果，融合提供了一种显着减少操作员负担的方法。边缘情况是无法在没有错误警报的情况下在任何单个视图中可靠地检测到的缺陷。在这里，显示了某些融合方法可以减少误报进行检测。在这种情况下，融合允许扩展 NDE 能力，对工程资产的设计和操作具有潜在影响。