Abstract
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.
Highlights
Non Destructive Testing (NDT) is an integral part of the assessment of quality and structural integrity in engineering components in-service and post-manufacture
While in [30], the surrogate being used is Polynomial Chaos model, this paper models spatial variation using a Gaussian Process (GP); the analysis of Probability of Detection (PoD), would follow similar lines to those of [30]
A fully data-driven framework for autonomous inspection based on a Bayesian optimisation over robust novelty indices has been presented and demonstrated on an NDT data set consisting of ultrasound measurements on a high-value aerospace composite specimen
Summary
Non Destructive Testing (NDT) is an integral part of the assessment of quality and structural integrity in engineering components in-service and post-manufacture. Most NDT is carried out manually, recent increased attention to robotic-based inspection [1] promises to change matters. This use of robotics is a major boon to inspecting high-value and/ or safety-critical assets; it removes humans from harm when inspections are needed in harsh and dangerous environments. Whilst the motivations for robotic NDT are clear, and research is under-way [1,2], little or nothing has been done in the way of performing autonomous, as opposed to automated, inspections. The data collected has to be reported back to a human, or to further post-processing algorithms in order to assess the
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