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This paper presents a demonstrative application of a forward model-driven approach to structural health monitoring (SHM), incorporating hierarchical validation methods. A key tenet of the approach is that an SHM system can be constructed that is capable of diagnosing damage at the full system level, without full system damage-state data having been used in its development; achieving this would be highly impactful as the system-level damage state data is generally not feasible to acquire (previous SHM methods such as data-driven SHM have been hindered by their dependence on these data). This is achieved by carrying out validation activities on the damage model at the subassembly level of the structure. The particular focus of the present paper is on damage detection and assessment, although the approach offers a natural basis for extension to other damage identification activities such as damage location and prognosis. The present study focuses on two of the key elements of the model-driven approach: validation of the predictive substructure models and their application in the assembled model. The ideas discussed are demonstrated in a case study based on a laboratory-scale truss bridge structure. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: The main problem associated with pattern recognition based approaches to Structural Health Monitoring (SHM) is that damage localisation and quantification almost always require supervised learning. In the case of high-value engineering structures like aircraft, it is simply not possible to generate the training data associated with damage by experiment. It is also unlikely that data can always be generated by simulation as the models required would often need to be of such high fidelity that the costs of development and the run-times would again be prohibitive. The object of this paper is to explore the potential of a simple experimental strategy, which involves adding masses to the structure, in the attempt to extract features for novelty detection. The idea itself is not presented as revolutionary based on the fact that adding masses has been considered as a case of damage before, however, an in-depth investigation of its suitability for guiding feature selection is presented here. The approach is illustrated first on a simple structure by using data generated from a finite-element (FE) simulation and then validated experimentally on a more complicated laboratory structure. Simulated damage, in the form of a loss in the stiffness in the case of the numerical model and of a saw-cut in the case of the structure is used for comparison. The results show similar patterns in both cases which suggests a potential use of the method for higher level damage detection. [Copyright &y& Elsevier]