Compressed Sensing: From Big Data to Relevant Data

Though the ever-increasing availability of digital data in the context of NDE 4.0 is mostly considered a blessing, it can turn to a curse quite rapidly: managing large amounts of data puts a burden on the sensor devices in terms of sampling and transmission, the networks, as well as the server infrastructure in terms of storing, maintaining, and accessing the data. Yet, NDE data can be highly redundant so the storage of massive amounts of data may indeed be wasteful. This is the main reason why focusing on relevant data as early as possible in the NDE process is highly advocated in the context of NDE 4.0. This chapter introduces Compressed Sensing as a potential approach to put this vision to practice. Compressed Sensing theory has shown that sampling signals with sampling rates that are significantly below the Shannon-Nyquist rate is possible without loss of information, provided that prior knowledge about the signals to be acquired is available. In fact, we may sample as low as the actual information rate if our prior knowledge is sufficiently accurate. In the NDE 4.0 context, prior knowledge can stem from the known inspection task and geometry but it can also include previous recordings of the same piece (such as in Structural Health Monitoring), information stored in the digital product memory along the products' life cycle, or predictions generated through the products' digital twins. In addition to data reduction, reconstruction algorithms developed in the Compressed Sensing community can be applied for enhanced processing of NDE data, providing added value in terms of accuracy or reliability. The chapter introduces Compressed Sensing basics and gives some concrete examples of its application in the NDE 4.0 context, in particular for ultrasound.