On Model- and Data-based Approaches to Structural Health Monitoring

Structural Heath Monitoring (SHM) is the term applied to the process of periodically monitoring the state of a structural system with the aim of diagnosing damage in the structure. Over the course of the past several decades there has been ongoing interest in approaches to the problem of SHM. This attention has been sustained by the belief that SHM will allow substantial economic and life-safety benefits to be realised across a wide range of applications. Several numerical and laboratory implementations have been successfully demonstrated. However, despite this research effort, real-world applications of SHM as originally envisaged are somewhat rare. Numerous technical barriers to the broader application of SHM methods have been identified, namely: severe restrictions on the availability of damaged-state data in real-world scenarios; difficulties associated with the numerical modelling of physical systems; and limited understanding of the physical effect of system inputs (including environmental and operational loads). This thesis focuses on the roles of law-based and data-based modelling in current applications of. First, established approaches to model-based SHM are introduced, with the aid of an exemplar ‘wingbox’ structure. The study highlights the degree of difficulty associated with applying model-updating-based methods and with producing numerical models capable of accurately predicting changes in structural response due to damage. These difficulties motivate the investigation of non-deterministic, predictive modelling of structural responses taking into account both experimental and modelling uncertainties. Secondly, a data-based approach to multiple-site damage location is introduced, which may allow the quantity of experimental data required for classifier training to be drastically reduced. A conclusion of the above research is the identification of hybrid approaches, in which a forward-mode law-based model informs a data-based damage identification scheme, as an area for future wor

Emerging trends in optimal structural health monitoring system design: From sensor placement to system evaluation

This paper presents a review of advances in the field of Sensor Placement Optimisation (SPO) strategies for Structural Health Monitoring (SHM). This task has received a great deal of attention in the research literature, from initial foundations in the control engineering literature to adoption in a modal or system identification context in the structural dynamics community. Recent years have seen an increasing focus on methods that are specific to damage identification, with the maximisation of correct classification outcomes being prioritised. The objectives of this article are to present the SPO for SHM problem, to provide an overview of the current state of the art in this area, and to identify promising emergent trends within the literature. The key conclusions drawn are that there remains a great deal of scope for research in a number of key areas, including the development of methods that promote robustness to modelling uncertainty, benign effects within measured data, and failures within the sensor network. There also remains a paucity of studies that demonstrate practical, experimental evaluation of developed SHM system designs. Finally, it is argued that the pursuit of novel or highly efficient optimisation methods may be considered to be of secondary importance in an SPO context, given that the optimisation effort is expended at the design stage

Studio 55: Library makerspace with a difference

AUT Library launched Studio 55, the Library Makerspace in 2017, the first in a New Zealand university library. While the concept of a library makerspace is not unique our approach has been a little different. We have adopted a more ‘low-tech’ approach in that we don’t have a 3D printer and other more ‘hi-tech’ equipment that is fairly standard in most makerspaces. It is a highly visible space that is open to all to ‘think, make, do’ and share skills, ideas and work together across disciplines. It is designed to engage the community in new ways of working and learning As we are operating in a constrained budget environment we were only able to allocate 0.2 FTE position to coordinate activities. Further support is provided by an active and competent Makerspace Operations Group comprised of staff from across the Library. We were also fortunate to have an Artist-in-Residence, funded by Student Services, based in the space in the latter part of the 2017. The workshops he offered greatly enhanced the range of activities offered. This presentation will discuss our approach, the workshops held, the learnings to date and a way forward

A probabilistic framework for forward model-driven SHM

A challenge for many structural health monitoring (SHM) technologies is the lack of available damage state data. This problem arises due to cost implications of damaging a structure in addition to issues associated with the feasibility and safety of testing a structure in multiple damage scenarios. Many data-driven approaches to SHM are successful when the appropriate damage state data is available, however the problem of obtaining data for various damage states of interest restricts their use in industry. Forward model-driven approaches to SHM seek to aid this challenge. This methodology uses validated physical models to generate predictions of the system at different damage states, providing machine learning strategies with training data, to infer decision bounds. An ideal forward model-driven SHM framework is one in which one or more physical models are able to produce predictions that are statistically representative of data obtained from the physical structure. Validation of these physical models requires observational data. As a result, validation is performed on a component or sub-system level where damage state data can be more easily obtained. This methodology requires the combination of several low-level physical models via a multi-level uncertainty integration technique. This paper outlines such a framework using uncertainty quantification technologies and statistical methods for combining low-level probabilistic models whilst accounting of discrepancies that may occur in interactions with other low-level models. The method contains several statistical techniques for accounting for model discrepancies that may occur at any point throughout the modelling process. Model discrepancies arise due to missing physics or simplifications and result in the model deviating from the observed physics even when the ‘true’ parameters of the model are known. By accounting for model discrepancies throughout the framework the approach allows for further insight into model form errors whilst also improving the techniques ability to produce statistically representative predictions across damage states. The paper presents the key stages highlighting the relevant technologies and application considerations. Additionally, a discussion of integration with current data-driven approaches and the appropriate machine learning tools is given for a forward model-driven SHM approach

Bayesian history matching for structural dynamics applications

Computer models provide useful tools in understanding and predicting quantities of interest for structural dynamics. Although computer models (simulators) are useful for a specific context, each will contain some level of model-form error. These model-form errors arise for several reasons e.g., numerical approximations to a solution, simplifications of known physics, an inability to model all relevant physics etc. These errors form part of model discrepancy; the difference between observational data and simulator outputs, given the ‘true’ parameters are known. If model discrepancy is not considered during calibration, any inferred parameters will be biased and predictive performance may be poor. Bayesian history matching (BHM) is a technique for calibrating simulators under the assumption that additive model discrepancy exists. This ‘likelihood-free’ approach iteratively assesses the input space using emulators of the simulator and identifies parameters that could have ‘plausibly’ produced target outputs given prior uncertainties. This paper presents, for the first time, the application of BHM in a structural dynamics context. Furthermore, a novel method is provided that utilises Gaussian Process (GP) regression in order to infer the missing model discrepancy functionally from the outputs of BHM. Finally, a demonstration of the effectiveness of the approach is provided for an experimental representative five storey building structure

The use of pseudo-faults for damage location in SHM: An experimental investigation on a Piper Tomahawk aircraft wing

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The application of pattern recognition-based approaches in damage localisation and quantification will eventually require the use of some kind of supervised learning algorithm. The use, and most importantly, the success of such algorithms will depend critically on the availability of data from all possible damage states for training. It is perhaps well known that the availability of damage data through destructive means cannot generally be afforded in the case of high value engineering structures outside laboratory conditions. This paper presents the attempt to use added masses in order to identify features suitable for training supervised learning algorithms and then to test the trained classifiers with damage data, with the ultimate purpose of damage localisation. In order to test the approach of adding masses, two separate cases of a dual-class classification problem, representing two distinct locations, and a three-class problem representing three distinct locations, are examined with the help of a full-scale aircraft wing. It was found that an excellent rate of correct classification could be achieved in both the dual-class and three-class cases. However, it was also found that the rate of correct classification was sensitive to the choices made in training the supervised learning algorithm. The results for the dual-class problem demonstrated a comparatively high level of robustness to these choices with a substantially lower robustness found in the three-class case

A report on the 6th European Conference on Structural Control

A short report is provided on the 6th European Conference on Structural Control which took place in Sheffield from 11–13 July 201

Robust methods for outlier detection and regression for SHM applications.

In this paper, robust statistical methods are presented for the data-based approach to structural health monitoring (SHM). The discussion initially focuses on the high level removal of the ‘masking effect’ of inclusive outliers. Multiple outliers commonly occur when novelty detection in the form of unsupervised learning is utilised as a means of damage diagnosis; then benign variations in the operating or environmental conditions of the structure must be handled very carefully, as it is possible that they can lead to false alarms. It is shown that recent developments in the field of robust regression can provide a means of exploring and visualising SHM data as a tool for exploring the different characteristics of outliers, and removing the effects of benign variations. The paper is not, in any sense, a survey; it is an overview and summary of recent work by the authors

A probabilistic risk-based decision framework for structural health monitoring

Obtaining the ability to make informed decisions regarding the operation and maintenance of structures, provides a major incentive for the implementation of structural health monitoring (SHM) systems. Probabilistic risk assessment (PRA) is an established methodology that allows engineers to make risk-informed decisions regarding the design and operation of safety-critical and high-value assets in industries such as nuclear and aerospace. The current paper aims to formulate a risk-based decision framework for structural health monitoring that combines elements of PRA with the existing SHM paradigm. As an apt tool for reasoning and decision-making under uncertainty, probabilistic graphical models serve as the foundation of the framework. The framework involves modelling failure modes of structures as Bayesian network representations of fault trees and then assigning costs or utilities to the failure events. The fault trees allow for information to pass from probabilistic classifiers to influence diagram representations of decision processes whilst also providing nodes within the graphical model that may be queried to obtain marginal probability distributions over local damage states within a structure. Optimal courses of action for structures are selected by determining the strategies that maximise expected utility. The risk-based framework is demonstrated on a realistic truss-like structure and supported by experimental data. Finally, a discussion of the risk-based approach is made and further challenges pertaining to decision-making processes in the context of SHM are identified