Autonomous ultrasonic inspection using Bayesian optimisation and robust outlier analysis
Autonomous ultrasonic inspection using Bayesian optimisation and robust outlier analysis
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DOI：
10.1016/j.ymssp.2020.106897
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发表时间：
2020-11-01
影响因子：
8.4
通讯作者：
Cross, E. J.
中科院分区：
文献类型：
--
作者：
Fuentes, R.;Gardner, P.;Cross, E. J.
机器人技术的使用开始在无损检测（NDT）中自动化数据收集过程中发挥关键作用。自动化的使用迅速增加，导致大量数据的收集，这使得人类解析其中包含的信息变得低效，甚至不可行。本文提出了一种解决这个问题的方法，使无损检测数据采集的过程中，一个自主的，而不是一个自动的。为了实现这一目标，机器人数据采集任务被视为一个优化问题，其中一个试图找到具有最高损伤指示的位置。由此产生的算法结合了数据驱动的结构健康监测（SHM）领域的损伤检测技术与不确定性量化的新思想，使优化程序的概率。该算法是顺序的;在每次迭代时做出关于进行观察的下一个最佳物理位置的决定。这是通过模拟一个二维领域的新奇指数在一个部分/结构，这是从一个强大的离群值分析程序。这种自主方法的价值在于输出不仅是测量数据，而且是NDT检测中最需要的信息-部件包含损坏的概率。此外，该算法还最大限度地减少了所需的观测数量，从而最大限度地减少了数据收集的时间和成本。(C)2020年，任作家。爱思唯尔有限公司出版
The use of robotics is beginning to play a key role in automating the data collection process in Non Destructive Testing (NDT). Increasing the use of automation quickly leads to the gathering of large quantities of data, which makes it inefficient, perhaps even infeasible, for a human to parse the information contained in them. This paper presents a solution to this problem by making the process of NDT data acquisition an autonomous one as opposed to an automatic one. In order to achieve this, the robotic data acquisition task is treated as an optimisation problem, where one seeks to find locations with the highest indication of damage. The resulting algorithm combines damage detection technology from the field of data-driven Structural Health Monitoring (SHM) with novel ideas in uncertainty quantification which enable the optimisation routine to be probabilistic. The algorithm is sequential; a decision is made at every iteration regarding the next optimal physical location for making an observation. This is achieved by modelling a two-dimensional field of novelty indices across a part/structure which is derived from a robust outlier analysis procedure. The value of this autonomous approach is that the output is not only measured data, but the most desirable information from an NDT inspection - the probability that a component contains damage. Furthermore, the algorithm also minimises the number of observations required, thus minimising the time and cost of data gathering. (C) 2020 The Authors. Published by Elsevier Ltd.