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

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

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

On Treed Gaussian Processes and piecewise-linear NARX modelling

In the scope of nonlinear system identification, traditional parametric models are widely adopted as simplifying approaches to modelling the complexity of nonlinearity. However, many high-order parametric models are disadvantaged due to their inherent demand for model detection and their tendency to overfit in the absence of additional validation processes. Nonparametric models, such as the Gaussian Process (GP), though being naturally exempt from model detection, can involve expensive procedures of model optimisation. This article presents a Linear Kernel Chipman-based Treed Gaussian Processes (LK-CTGP), which is essentially an assembly of simple linear parametric models using a decision tree framework, to model nonlinear systems. The piecewise-linear structure of the LK-CTGP offers a natural geometric solution to modelling nonlinear systems, where no model detection is required. The essence of simplicity from the traditional parametric model is also completely retained within each of the submodels. The effectiveness of the LK-CTGP is illustrated here via a number of case studies from simple synthetic data to experimental data, on which Nonlinear Autoregressive eXogenous (NARX) systems will built from the data for in-depth study

Hierarchical model verification and validation for structural health monitoring using dynamic substructuring

Despite the success of data-based methods in structural health monitoring (SHM), these approaches often suffer from a lack of training data, which can be difficult to acquire for several reasons: damage-state data acquisition can be infeasible, structures may be unique and only tested in situ, sensor placement can cause issues, certain structures cannot be tested in controllable laboratory conditions and representative environmental conditions can be difficult to simulate. Training data can be simulated using physics-based models. However, this is dependent on model verification and validation (V&V), meaning assembly-level data is still required. Hierarchical V&V is a novel technique in the field of SHM. The aim of hierarchical V&V is to remove the necessity for assembly-level validation data. Instead, the process entails the V&V of subassembly-level models, which are then combined to produce an assembly-level model using dynamic substructuring (DS). This simplifies the data acquisition process in order to reduce the associated difficulties and costs. This paper focuses on the role of DS in the hierarchical V&V process for SHM. DS allows substructures to be used to create an assembly model, and for simultaneous uncertainty propagation. This allows confidence to be established in the assembled models without requiring assembly-level data

On evolutionary system identification with applications to nonlinear benchmarks

This paper presents a record of the participation of the authors in a workshop on nonlinear system identification held in 2016. It provides a summary of a keynote lecture by one of the authors and also gives an account of how the authors developed identification strategies and methods for a number of benchmark nonlinear systems presented as challenges, before and during the workshop. It is argued here that more general frameworks are now emerging for nonlinear system identification, which are capable of addressing substantial ranges of problems. One of these frameworks is based on evolutionary optimisation (EO); it is a framework developed by the authors in previous papers and extended here. As one might expect from the ‘no-free-lunch’ theorem for optimisation, the methodology is not particularly sensitive to the particular (EO) algorithm used, and a number of different variants are presented in this paper, some used for the first time in system identification problems, which show equal capability. In fact, the EO approach advocated in this paper succeeded in finding the best solutions to two of the three benchmark problems which motivated the workshop. The paper provides considerable discussion on the approaches used and makes a number of suggestions regarding best practice; one of the major new opportunities identified here concerns the application of grey-box models which combine the insight of any prior physical-law based models (white box) with the power of machine learners with universal approximation properties (black box)

On robustness of optimal sensor placement to environmental variation for SHM

One challenge in establishing an effective structural health monitoring (SHM) system is the impact of environmental variability on damage identification. It is therefore, advantageous to consider any environmental effects when conducting sensor placement optimisation (SPO). One approach to this problem is to check the robustness of SPO technique to environmental variations and consider whether it is necessary to take account of these environmental factors as part of the optimisation process. This paper will study the robustness of an SPO method to variations in the ambient temperature of the structure. Two kinds of data, including the mode shapes and the Mahalanobis squared-distance (MSD), from tests on a glider wing structure are used as features for SPO separately. This structure was set up and tested in different health states across a series of controlled temperatures. The results show that the SPO results obtained via the mode shapes are robust to the temperature variation, while the SPO results corresponding to MSD are sensitive to temperature changes

On improved fail-safe sensor distributions for a structural health monitoring system

Sensor placement optimization (SPO) is usually applied during the structural health monitoring sensor system design process to collect effective data. However, the failure of a sensor may significantly affect the expected performance of the entire system. Therefore, it is necessary to study the optimal sensor placement considering the possibility of sensor failure. In this article, the research focusses on an SPO giving a fail-safe sensor distribution, whose sub-distributions still have good performance. The performance of the fail-safe sensor distribution with multiple sensors placed in the same position will also be studied. The adopted data sets include the mode shapes and corresponding labels of structural states from a series of tests on a glider wing. A genetic algorithm is used to search for sensor deployments, and the partial results are validated by an exhaustive search. Two types of optimization objectives are investigated, one for modal identification and the other for damage identification. The results show that the proposed fail-safe sensor optimization method is beneficial for balancing the system performance before and after sensor failure