Improving sensor placement optimisation robustness to environmental variations and sensor failures for structural health monitoring systems

The installation of structural health monitoring (SHM) systems based on machine learning algorithms on structures has been of constant interest. The application of this kind of SHM system can facilitate decisions regarding maintenance and the remaining useful life of the structure in a more automatic and convenient way. As part of the SHM system to collect information, the sensor system can be optimally designed to improve the performance of the final system. In this thesis, the work focuses on how to consider the effects of environment and sensor failures during the sensor placement optimisation (SPO) to build a more robust and effective monitoring system. Since the availability of data during the design phase varies widely from project to project and there are no studies or specifications that provide specific guidance, not much research has been done on the design of such sensor systems, which require reliable simulated or measured data to be available during the design phase. Considering the different levels of data accessibility at the design phase, this thesis proposes a series of strategies for the optimal design of sensor systems for SHM systems from a machine-learning perspective. The first main content of this thesis is hierarchical assessment criteria of designed-system performance to balance the computational feasibility and visualisation of the final system performance. At the stage after data is collected, machine learning model results are often used as a criterion, whose acquisition is usually time-consuming. At the same time, higher data accuracy is required. Therefore, the criteria used in the design of sensor systems are divided into different tiers. The criteria for the initial stage can be abstracted from the purpose of the applied machine learning model to significantly reduce the number of candidate designs. The criteria for the final stage can be similar to those used in the stage after data is collected. Whether or not to use criteria from all tiers depends on the level of data availability. It can be found that more work on the optimisation design of the sensor system can be done at the initial stage of the hierarchical design framework. Therefore, the other three main contents of this thesis are developed at this stage. Considering different levels of data availability, supervised and unsupervised correlation-based strategies to evaluate sensor combinations are proposed, including the evaluation criterion and the fast calculation methods of this criterion. Sensor combinations can be ranked even if only the healthy state data are accessible. To account for the effects of environmental variations, two SPO strategies based on approaches to extracting robust features are proposed, and an appropriate criterion that can be used is also introduced. These two strategies cover both situations where environmental change information is available and not. To consider the sensor-failure effect in the SPO process, another two strategies, namely fail-safe sensor optimisation or fail-safe optimisation with redundancy, are proposed in this thesis, both of which can take into account the performance of the designed system before and after the failure of some critical sensors. Different assessment criteria are adopted to demonstrate the generality of these strategies.