A bio-inspired knowledge and data perspective of smart sensor systems
TL;DR: This work shows the potential of applying the layered approach to Structural Health Monitoring, by introducing a layered approach and addressing how various sensor source can be combined and how decision can be made regarding the data be used and discarded.
The past decade has shown a trend to collect and mine large quantities of our data with the objective to assess the performance of materials and systems for various reasons. This rise of Artificial Intelligence has also largely affected the field of Structural Health Monitoring. A plethora of intelligent data science algorithms has been studied, developing the ability to recognise subtle changes in the (dynamic) behaviour of structures resulting from the presence of early stages of damage or deterioration. More recently, the direction has been altered again, towards inclusion of physics-based information. This is done on various levels and in different ways, but all with the shared objective to mitigate one of the most important drawbacks or limitations of pure data-centric methods: the limited performance in case of poor, incomplete, scares or non-representative data. This lack of data quality is not just solved by collecting more data and data with higher accuracy, but is also an inherent problem for monitoring systems of actual structures: maintenance interventions, securing the safe operation of the structure, imply hardly any data is collected of failed systems. In addition, the blunt collection of data may work counterproductive if it comes to practical implementation of these methods. The collection and processing of this data is certainly not free of charge, while the additional information (hence the value) of more data is often limited. Hence, research is directed towards selection of relevant and informative features, reducing the data demand, while maximizing the information output. Although these developments are doubtlessly of great value, a different perspective may provide new ideas on how to overcome some of the challenges of finding the right balance between knowledge and data to optimise the information output of a monitoring system. What is nature's perspective on how to extract information efficiently from large and diverse data sources? Human sense can be considered as a sensor system with diverse set of sensors and a large variety and flexibility in how these are connected and are mutually communicating. The human sensory system collects a lot of data, but the processing of this data differs fundamentally from the way data is processed in data-centric methods. It is true that a neural networks mimics to some extent the way a brain works: general patterns of behaviour are trained at first, after which new observations are mapped on these patterns to identify the observed behaviour. In reality, human brains use a limited part of the data to guess what is happening. Despite clear evidence that human brains can be fooled, the general functionality of the approach is fairly good to say the least. This work shows the potential of applying this method, by introducing a layered approach and addressing how various sensor source can be combined and how decision can be made regarding the data be used and discarded.