Department of Electrical and Computer Engineering, Faculty of Electrical Engineering, West Pomeranian University of Technology, Szczecin, al. Piastow 17, 70-310 Szczecin, Poland.
Sensors (Basel). 2018 Jun 29;18(7):2091. doi: 10.3390/s18072091.
Increasing the number of inspection sources creates an opportunity to combine information in order to properly set the operation of the entire system, not only in terms of such factors as reliability, confidence, or accuracy, but inspection time as well. In this paper, a magnetic sensor-array-based nondestructive system was applied to inspect defects inside circular-shaped steel elements. The experiments were carried out for various sensor network strategies, followed by the fusion of multisensor data for each case. In order to combine the measurements, first data registration and then four algorithms based on spatial and transformed representations of sensor signals were applied. In the case of spatial representation, the data were combined using an algorithm operating directly on input signals, allowing pooling of information. To build the transformed representation, a multiresolution analysis based on the Laplacian pyramid was used. Finally, the quality of the obtained results was assessed. The details of algorithms are given and the results are presented and discussed. It is shown that the application of data fusion rules for magnetic multisensor inspection systems can result in the growth of reliability of proper identification and classification of defects in steel elements depending on the utilized configuration of the sensor network.
增加检测源的数量为整合信息提供了机会，以便正确设置整个系统的运行，不仅要考虑可靠性、置信度或准确性等因素，还要考虑检测时间。本文应用基于磁传感器阵列的无损检测系统对圆形钢构件内部缺陷进行检测。针对不同的传感器网络策略进行了实验，然后对每种情况的多传感器数据进行融合。为了对测量结果进行融合，首先对数据进行配准，然后应用基于传感器信号空间和变换表示的四种算法。在空间表示的情况下，直接在输入信号上应用算法对数据进行组合，实现信息的汇集。为构建变换表示，使用基于拉普拉斯金字塔的多分辨率分析。最后，评估了获得结果的质量。给出了算法的细节，并展示和讨论了结果。结果表明，对于磁多传感器检测系统，应用数据融合规则可以提高正确识别和分类钢构件缺陷的可靠性，具体取决于所使用的传感器网络配置。
