## 1 Introduction  
Fatigue damage detection is one of the most important challenges that structural engineers encounter [1]. Although fatigue has been studied for a long time, a predictive framework to accurately and comprehensively estimate the fatigue life of a component is still elusive. This is mainly due to the massively vast parameter space that drives such failures in an operational setup [2]. Several factors, such as the load conditions, history, frequency, part geometry, presence of stress concentration factors, material microstructures, and defects, contribute to fatigue failure. While these factors may be accounted for to a certain extent through laboratory-based experiments to develop predictive models, they are often accompanied by unprecedented levels of uncertainties in operation. Hence, a comprehensive experimentation to understand their coupled effects can become overwhelmingly expensive to perform [3]. Analytical and computational modeling are often performed to augment the understanding of fatigue failures. However, owing to their computational cost, these numerical models are often carried out on reduced-scale geometries. Consequently, a quantifiable generalized framework that can predict the fatigue behavior of any new materials and manufacturing processes remains a major research focus to date [4]. A broad categorization of fatigue-related research is shown in Figure 1a. The focus is either toward a 'prediction' or a 'detection' framework. The prediction framework is mainly targeted toward influencing design criteria that can enable a fatigue-resistant component, whereas the detection framework is useful in a working environment. The studies in the prediction framework can be further divided into three main domains, viz., analytical, computational,and empirical. A recent review paper by [PERSON] et al. [5] summarizes four classes of analytical techniques that are pursued for this problem, viz., nominal stress approaches, local stress-strain approaches, critical distance theories, and weighting control-parameter-based approaches. High-fidelity computational frameworks are being mainly developed using crystal plasticity simulations [6; 7]. Empirical frameworks, the oldest and comparatively more error-prone among the three, have been documented through several design criteria [8]. The detection framework on the other hand, is targeted toward developing better sensing mechanisms [9] or improving the data analysis therein [10]. The detection framework provides real-time information about a component's health and, therefore, is critical in ensuring the safe operation of fatigue-critical components in operation [11]. The elemental philosophy of the sensor-based approach is underpinned by the sensors' ability to continuously stream data that has the health information of a component encoded in it. By analyzing this stream of data via data-driven approaches, reliable strategies can be developed to detect fatigue damage in real-time [12]. Over the past few decades, rapid growth of deep learning methodologies has ushered a new era in damage detection analysis algorithms [10; 13; 14; 15; 16]. As summarized by [PERSON] et al. [15], the state-of-the-art deep learning methods, such as autoencoders, restricted Boltzmann machines, deep Boltzmann machines, convolutional neural nets (CNNs), and recurrent neural nets (RNNs), have shown promising applications to this field. CNNs have shown a particular proclivity to problems that have dealt with imaging datasets [14; 16]. However, in the majority of complex structures, such as aerospace or automobiles, the reliance is solely on time-series-based sensors, which can be processed with varied methods. For instance, through an autoencoder, acoustic emission data were analyzed to localize damage [17]. With Bayesian Graph Neural Nets, [PERSON] et al. [18] demonstrated the application of strain gauge sensors. [PERSON] et al. [19] studied damage detection in spot welds using ultrasonics and artificial neural nets (ANNs).