###### Abstract
[24] proposed the use of the peak-to-peak value of the piston rod displacement for fracture detection, correlating it with increased vibration. On the other hand, [PERSON] et al. [12] used acoustic emission (AE) and a fatigue testing machine to monitor cracks and detect piston rod fractures. In our previous investigations, we systematically described the fault propagation chain associated with piston rod thread loosening due to lateral forces and piston drop due to rider band wear, ultimately leading to piston rod fracture. This comprehensive analysis was carried out from a failure source perspective using both multibody dynamics analysis and finite element analysis [25]. Subsequent research has been extended by introducing the characteristic spectrum of the vibration signals, which enables the quantitative diagnosis of piston rod thread loosening [26]. However, the monitoring of crack initiation and propagation remains a critical component of piston rod fracture prevention. Despite current research advances in the comprehension of failure mechanisms and causative fault diagnosis methods, there is still a significant research gap in the diagnosis of piston rod cracks during compressor operation. The piston rod in a reciprocating compressor is subjected to alternating tensile and compressive loads during its operating cycles. Previous studies have predominantly linked piston rod failure to factors such as reduced fatigue life due to manufacturing defects [27], improper installation [28], additional external stress concentration [29; 30], and changes in the operating conditions [31]. Fatigue as a phenomenon occurs across scales and loads, from the microscale to the macroscale [32]. Depending on the material, a significant portion (50-90%) of the fatigue life is exhausted before cracking occurs [33]. Once fatigue cracks occur, the stress state of the piston rod gradually deteriorates, posing a threat to the safety of UGS operations. AE technology serves as one of the structural health monitoring techniques capable of detecting crack growth in real time using highly sensitive sensors. AE involves the propagation of elastic waves within a material, driven by structural changes induced by plastic deformation, crack growth, and the redistribution of internal stresses, ultimately leading to the release of energy [34]. This technology is currently being applied to various materials, including metals [35], concrete [36], composites [37], and polychene [38], and to various engineering structures, such as bridges [39], pressure vessels [40], transformers [41], aircraft structures [42], and railway axles [43], for damage monitoring and identification. In the field of compressors, AE has been mainly used to detect valve leakage [14; 44]. Several studies have shown that AE parameters extracted from detected AE waves are closely related to the progression of fatigue damage. For example, [PERSON] et al. [45] identified cracks in metal specimens by quantifying AE information and found a transition from a low-frequency continuous-type waveform to a high-frequency burst-type waveform associated with crack initiation. [PERSON] et al. [46] demonstrated a linear quantitative relationship on a logarithmic scale between the fatigue crack growth rate and AE parameters, with the AE energy providing a qualitative identification of fatigue damage. Despite these findings, the mechanisms underlying the effect of complex load excitation on piston rod crack propagation in compressors and the methods for extracting characteristics from AE signals during the fatigue fracture of piston rods remain unexplored. Monitoring the fracture behaviour of piston rods is an important research topic in compressor dependability, particularly as the failure of piston rods in scenarios with flammable and explosive gases can result in significant economic losses and pose a significant risk to the safety of personnel.