Abstract
The prospect of informed and optimal decision-making regarding the operation and maintenance (O&M) of structures provides impetus to the development of structural health monitoring (SHM) systems. A probabilistic risk-based framework for decision-making has already been proposed. The framework comprises four key submodels: the utility model, the failure-modes model, the statistical classifier, and the transition model. The cost model consists of utility functions that specify the costs of actions and structural failures. The failure-modes model defines the failure modes of a structure as combinations of component and substructure failures via fault trees. The statistical classifier and transition model are models that predict the current and future health-states of a structure, respectively. Within the data-driven statistical pattern recognition (SPR) approach to SHM, these predictive models are determined using machine learning techniques. However, in order to learn these models, measured data from the structure of interest are required. Unfortunately, these data are seldom available across the range of environmental and operational conditions necessary to ensure good generalisation of the model.
Recently, technologies have been developed that overcome this challenge, by extending SHM to populations of structures, such that valuable knowledge may be transferred between instances of structures that are sufficiently similar. This new approach is termed population-based structural heath monitoring (PBSHM).
The current paper presents a formal representation of populations of structures, such that risk-based decision processes may be specified within them. The population-based representation is an extension to the hierarchical representation of a structure used within the probabilistic risk-based decision framework to define fault trees. The result is a series, consisting of systems of systems ranging from the individual component level up to an inventory of heterogeneous populations. The current paper considers an inventory of wind farms as a motivating example and highlights the inferences and decisions that can be made within the hierarchical representation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Farrar, C.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. Wiley, Hoboken (2013)
Rytter, A.: Vibration Based Inspection of Civil Engineering Structures. Ph.D. Thesis, Aalborg University (1993)
Schöbi, R., Chatzi, E.N.: Maintenance planning using continuous-state partially observable Markov decision processes and non-linear action models processes and non-linear action models. Struct. Infrastruct. Eng. 12(8), 977–994 (2016)
Vega, M.A., Todd, M.D.: A variational Bayesian neural network for structural health monitoring and cost-informed decision-making in miter gates. Struct. Health Monit. 21, 1475921720904543 (2020)
Hughes, A.J., Barthorpe, R.J., Dervilis, N., Farrar, C.R., Worden, K.: A probabilistic risk-based decision framework for structural health monitoring. Mech. Syst. Signal Process. 150, 107339 (2021)
Bull, L.A., Rogers, T.J., Wickramarachchi, C., Cross, E.J., Worden, K., Dervilis, N.: Probabilistic active learning: an online framework for structural health monitoring. Mech. Syst. Signal Process. 134, 106294 (2019)
Hughes, A.J., Bull, L.A., Gardner, P., Barthorpe, R.J., Dervilis, N., Worden, K.: On risk-based active learning for structural health monitoring. Mech. Syst. Signal Process. 167, 108569 (2022)
Hughes, A.J., Bull, L.A., Gardner, P., Dervilis, N., Worden, K.: On robust risk-based active-learning algorithms for enhanced decision support. Mech. Syst. Signal Process. 181, 109502 (2022)
Vega, M.A., Hu, Z., Todd, M.D.: Optimal maintenance decisions for deteriorating quoin blocks in miter gates subject to uncertainty in the condition rating protocol. Reliabil. Eng. Syst. Safety 204, 107147 (2020)
Bull, L.A., Gardner, P., Gosliga, J., Rogers, T.J., Dervilis, N., Cross, E.J., Papatheou, E., Maguire, A.E., Campos, C., Worden, K.: Foundations of population-based SHM, part I: homogeneous populations and forms. Mech. Syst. Signal Process. 148, 107141 (2021)
Gosliga, J., Gardner, P.A., Bull, L.A., Dervilis, N., Worden, K.: Foundations of population-based SHM, part II: heterogeneous populations - graphs, networks, and communities. Mech. Syst. Signal Process. 148, 107144 (2021)
Gardner, P., Bull, L.A., Gosliga, J., Dervilis, N., Worden, K.: Foundations of population-based SHM, part III: heterogeneous populations - mapping and transfer. Mech. Syst. Signal Process. 148, 107142 (2021)
Tsialiamanis, G., Mylonas, C., Chatzi, E., Dervilis, N., Wagg, D.J., Worden, K.: Foundations of population-based SHM, part IV: the geometry of spaces of structures and their feature spaces. Mech. Syst. Signal Process. 157, 107692 (2021)
Worden, K., Bull, L.A., Gardner, P., Gosliga, J., Rogers, T.J., Cross, E.J., Papatheou, E., Lin, W., Dervilis, N.: A brief introduction to recent developments in population-based structural health monitoring. Front. Built Environ. 6, 146 (2020)
Wickramarachchi, C.T., Gosliga, J., Cross, E.J., Worden, K.: On the use of graph kernels for assessing similarity of structures in population-based structural health monitoring. In: Proceedings of the Thirteenth International Workshop on Structural Health Monitoring (2022)
Dhada, M., Girolami, M., Parlikad, A.K.: Anomaly detection in a fleet of industrial assets with hierarchical statistical modeling. Data-Centric Eng. 1, e21 (2020)
Sucar, L.E.: Probabilistic Graphical Models: Principles and Applications. Springer, London (2015)
Kjaerulff, U.B., Madsen, A.L.: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. Springer, New York (2008)
Bobbio, A., Portinale, L., Minichino, M., Ciancamerla, E.: Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliabil. Eng. Syst. Safety 71(3), 249–260 (2001)
Mahadevan, S., Zhang, R., Smith, N.: Bayesian networks for system reliability reassessment. Struct. Safety 23(3), 231–251 (2001)
Kamariotis, A., Chatzi, E., Straub, D.: Value of information from vibration-based structural health monitoring extracted via Bayesian model updating. Mech. Syst. Signal Process. 166, 108465 (2022)
Acknowledgements
The authors would like to gratefully acknowledge the support of the UK Engineering and Physical Sciences Research Council (EPSRC) via grant references EP/W005816/1 and EP/R006768/1. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. KW would also like to acknowledge support via the EPSRC Established Career Fellowship EP/R003625/1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Society for Experimental Mechanics, Inc.
About this paper
Cite this paper
Hughes, A.J., Gardner, P., Worden, K. (2024). Towards Risk-Informed PBSHM: Populations as Hierarchical Systems. In: Noh, H.Y., Whelan, M., Harvey, P.S. (eds) Dynamics of Civil Structures, Volume 2. SEM 2023. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-031-36663-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-36663-5_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36662-8
Online ISBN: 978-3-031-36663-5
eBook Packages: EngineeringEngineering (R0)