Autonomous ultrasonic inspection using Bayesian optimisation and robust outlier analysis

Fuentes, R. and Gardner, P. and Mineo, C. and Rogers, T.J. and Pierce, S.G. and Worden, K. and Dervilis, N. and Cross, Elizabeth (2020) Autonomous ultrasonic inspection using Bayesian optimisation and robust outlier analysis. Mechanical Systems and Signal Processing, 145. 106897. ISSN 0888-3270

[img]
Preview
 Text (Fuentes-etal-MSSP-2020-Autonomous-ultrasonic-inspection-using-Bayesian-optimisation)
Fuentes_etal_MSSP_2020_Autonomous_ultrasonic_inspection_using_Bayesian_optimisation.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (3MB)| Preview

    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.

    Creators(s): Fuentes, R., Gardner, P., Mineo, C. ORCID logoORCID: https://orcid.org/0000-0002-5086-366X, Rogers, T.J., Pierce, S.G. ORCID logoORCID: https://orcid.org/0000-0003-0312-8766, Worden, K., Dervilis, N. and Cross, Elizabeth;
    Item type:Article
    ID code:72351
    Keywords:non-destructive testing (NDT), ultrasound, Gaussian process (GP) regression, Bayesian optimisation, outlier analysis, Electrical engineering. Electronics Nuclear engineering, Signal Processing, Civil and Structural Engineering, Mechanical Engineering, Control and Systems Engineering, Computer Science Applications, Aerospace Engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Technology and Innovation Centre > Sensors and Asset Management
    Depositing user: Pure Administrator
    Date deposited:13 May 2020 13:47
    Last modified:14 Jan 2021 02:39
    URI:https://strathprints.strath.ac.uk/id/eprint/72351
    Export data:
    CORE (COnnecting REpositories)