## 1 Introduction  
Offshore operators are constantly concerned by the safety and integrity of their assets, due to the high stakes involved. Ageing structures notably pose a significant threat to human lives and can incur exorbitant costs when unplanned shutdowns or catastrophic failures occur. Furthermore, in addition to the structural challenges that onshore structures experience, offshore assets withstand harsh marine environments as a result of severe storms, highly corroding sea water, seaquakes, and cyclic wave loading [1,2]. In this context, structural health monitoring (SHM) tools have been developed over the last few decades to mitigate such risks and offer continuous monitoring solutions that can be used in different industries. One of the major problems in the design of offshore equipment is fatigue damage accumulation. Although this topic has been extensively studied in the literature, theoretically, numerically, and experimentally [3, 4, 5, 6, 7, 8, 9], the available inspection and monitoring technologies developed to date have not been able to fully overcome the severe environmental challenges associated with offshore service activities. Remotely operated vehicles (ROVs) have been widely used since the 1970s but face serious difficulties, despite technological advances, due to the highly unpredictable operating environment characterised by poor visibility and unstable conditions [10; 11]. For fatigue damage detection in structural applications in general, several sensing techniques have been developed [12], including guided ultrasonic waves [13; 14], fibre Bragg gratings [15; 16; 17], strain gauges [18], and piezoelectric sensors [19]. Acoustic emission (AE) has also been proposed as a potential solution to detect and monitor cracking in structures, such as vessels and pipelines [20] and bridges [21]. [PERSON] et al. [22] found a correlation between crack evolution rates and the AE count rates for a narrow range of loading in steel specimens subject to tension. [PERSON] et al. [23] predicted the crack growth behaviour in compact tension steel specimens based on AE data. The AE data were filtered using specific techniques and obtained from AE sensors placed around the crack tip. The authors showed that an accurate life prediction model could be established when the absolute energy of the AE signals was analysed. Despite its limitations, it is now recognised that the AE technique is capable of monitoring fatigue crack initiation [24] and propagation [25] in steels and other metals. Amongst the offshore assets that are vulnerable to corrosion-enhanced fatigue damage, mooring chains are one of the most crucial mooring components used in permanently anchored structures [26]. Despite their importance, limited experimental work on mooring chains exists in the literature. Studies have been carried out on the chains' material microstructural properties [27; 28], whereas others have been numerical modelling oriented [29]. Few large-scale testing attempts have been made. [PERSON] et al. [30] conducted a 4-month feasibility study to establish the AE technique's capabilities in monitoring damage in mooring chain links subjected to stress corrosion cracking in artificial sea water. Although a metallurgical examination was not performed, the authors suggested that the AE technique has promising potential in monitoring fatigue cracking in mooring chains. The present work is a continuation of a research programme [30; 31; 32] aimed at identifying the key AE signal features for the prediction of fatigue crack growth in mooring chain links. The primary goal of this study was to investigate the applicability of using ultrasonic guided waves (UGW) and AE approaches for detecting and monitoring crack initiation, location, and propagation on a mooring chain. The study shows preliminary modelling simulations using different finite element analysis (FEA) methods. In this paper, in Section 2, a description of the full-scale test rig and the monitoring setup is presented.