Applied AI in instrumentation and measurement: The deep learning revolution

In the last few years, hardly a day goes by that we do not hear about the latest advancements and improvements that Artificial Intelligence (AI) has brought to a wide spectrum of domains: from technology and medicine to science and sociology, and many others. AI is one of the core enabling components of the fourth industrial revolution that we are currently witnessing, and the applications of AI are truly transforming our world and impacting all facets of society, economy, living, working, and technology. The field of Instrumentation and Measurement (IM) is no exception, and has already been impacted by Applied AI. In this article, we give an overview of Applied AI and its usage in IM. We then take a deeper look at the IM applications of one specific AI method: Deep Learning (DL), which has recently revolutionized the field of AI. Our survey of DL papers published in the IEEE Transactions on Instrumentation and Measurement (IEEE TIM) and IEEE Instrumentation Measurement Magazine showed that, since 2017, there is a very strong interest in applying DL methods to IM, in terms of measurement, calibration, and other IM challenges. In particular, of the 32 surveyed papers, 75% were published in 2017 or later, and a remarkable 50% were published in 2019 alone. Considering that 2019 was not yet finished when we were writing this article, the recent exponential interest in and impact of DL in IM is a very evident trend. We also found that although DL is used in a variety of IM topics, a considerable portion of DL in IM focuses on Vision Based Measurement (VBM) systems (around 28%) and fault/defect diagnosis/detection/prediction (around 25%). Finally, we found that Convolutional Neural Networks are the most widely used DL technique in IM, especially in VBM. But to explain all of the above findings, we first need to understand AI itself and what we mean by it in its applied context. So let us begin our discussion with Applied AI.