Improving Inspection Reliability through Data Fusion of Multi-View Array Data

该项目的目标是在包括核能发电和国防部门在内的一系列工业的安全关键部件的缺陷检测和表征方面取得逐步改进。这将通过现代超声阵列成像技术获得的组件内部多个视图的数据融合来实现。该团队之前的工作已经证明，将数据融合应用于由单元素探针进行的单独扫描获得的超声波数据时，检测性能提高了两个数量级。这是在一种情况下，预期的缺陷是小的，点状的夹杂物，大致均匀地散布在所有方向上。该项目将发展数据融合理念，以提高在更复杂的情况下从多视图阵列数据中检测缺陷的性能，在这些情况下，缺陷形态不能提前假设，散射模式可能具有强方向性。因此，该项目将必须解决将数据融合应用于多视图阵列数据的缺陷分类和大小的关键挑战。将制作演示软件，显示测试组件的图像，并根据它们由缺陷产生的可能性进行排序；然后，可以探测这些指示中的任何一个，以显示详细的分类（例如裂纹、空洞、夹杂物等）和尺寸信息。该项目得到了EDF、日立、BAE系统和AMEC Foster Wheeler的支持。

The objective of this project is to obtain a step-change improvement in the detection and characterisation of defects in safety-critical components across a range of industries including nuclear power generation and the defence sector. This will be achieved through data-fusion of the multiple views of a component's interior that can be obtained through modern ultrasonic array imaging techniques. Previous work by the team has demonstrated a two-order-of-magnitude improvement in detection performance when data fusion was applied to ultrasonic data obtained from separate scans performed with single-element probes. This was in a case where the expected defects were small, point-like inclusions that scatter roughly uniformly in all directions. The proposed project will develop the data-fusion philosophy for improving defect detection performance from multi-view array data in the much more complex case where the defect morphology cannot be assumed in advance and the scattering pattern may be strongly directional. Therefore, the project will necessarily address the critical challenges of applying data fusion to defect classification and sizing from multi-view array data. Demonstrator software will be produced that will show an image of the test component with indications ranked by the probability of them being produced by a defect; it will then be possible to probe any of these indications to show detailed classification (e.g. crack, void, inclusion etc.) and sizing information. The project is supported by EDF, Hitachi, BAE Systems and AMEC Foster Wheeler.