Emerging Trends in Optimal Structural Health Monitoring System Design: From Sensor Placement to System Evaluation

Abstract

<jats:p>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.</jats:p>

Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory

“…The optimization problem can be solved using continuous design variables accounting for the location of the sensors over the physical domain of the structure or discrete design variables accounting for the discrete locations (e.g., DOF at nodes for placing displacement/acceleration sensors or Gauss integration points for placing strains sensors in a finite element mesh). Global optimization algorithms [ 87 , 88 ] as well as stochastic optimization algorithms, such as CMA-ES [ 89 ] and genetic algorithms [ 45 , 90 , 91 , 92 , 93 ] can be employed in order to avoid premature convergence to a local optimum. Alternative heuristic forward and backward sequential sensor placement (FSSP/BSSP) algorithms [ 54 , 57 ] are effective in solving the optimization problem.…”

“…OSP techniques have been developed in the past for the purpose of extracting the most informative data from a given number of sensors. A recent article [ 45 ] reviews methods and optimization algorithms for optimizing the location of sensor in a structure. Selecting the most informative sensor configuration is often performed using information theory based approaches.…”

SSVM: An Ultra-Low-Power Strain Sensing and Visualization Module for Long-Term Structural Health Monitoring

“…Aging civil structures mostly suffer catastrophic failures as the performance and condition degrade over time due to environmental factors, loads (dead/live), and design errors [ 4 ]. SHM provides the necessary quantitative details [ 5 ] that facilitate condition assessment for decision making, i.e., whether to demolish or maintain the structure. Proper countermeasures regarding maintenance increase serviceability along with the assurance of safety.…”

On sensor optimisation for structural health monitoring robust to environmental variations

“…For this technique, the objective function (OF) should first be designed to evaluate the effectiveness of the data collected from sensors in a given arrangement. Currently, the commonly-used OFs are mainly focussed on maximising the performance of modal identification, parameter estimation schemes (Barthorpe and Worden, 2020;Papadimitriou, 2004;Huan and Marzouk, 2013). However, to improve the ability of an SHM system to identify the structural state, it is necessary to conduct some research on OFs for SPO that are linked directly to structural health-state identification.…”