## 1 Introduction  
Many industrial components are subjected to cumulative damages associated with cyclic loadings. In a damage-tolerant scenario, each mechanical component needs to retain its residual health, safety, and functionality as long as possible to avoid extra maintenance, which is expensive, difficult to perform, or even impossible, as it often requires extensive knowledge of potential damage evolution. Most mechanical systems are currently monitored during both scheduled and unscheduled maintenance by means of nondestructive inspection technologies (NDIs); however, in recent years, the problem of real-time monitoring of mechanically stressed components has become a critical research topic. Condition-monitoring techniques are of multiple natures, as described by [PERSON] et al. [1] The most intuitive is vibration analysis, which works with machinery vibration, and in which faulted elements promote different vibration spectra from healthy ones. Several authors have proposed experimental-based works dealing with vibration measurements. [PERSON] et al. [2] studied the effects of damages on gears and bearings. [PERSON] et al. [3] studied the effects of damages with noncontact techniques. [PERSON] et al. [4] studied the effects of the presence of tooth root cracks on plastic gears by means of neural networks [4]. [PERSON] et al. studied the surface-fatigue damages on worm gears [5]. [PERSON] et al. proposed a computational-based approach [6] instead. A similar work based on cyclostationary analysis was presented by [PERSON] et al. [7] and by [PERSON] et al. [8]. Acoustic-emission monitoring is an extension of vibration analysis. Although it suffers from background noise, which often yields useless results, it is simple and cost-effective. Lubricant analysis is one of the most challenging methods, as it requires oil sampling and further chemical analysis. It does not operate in real time; due to this characteristic, it may detects faults when it is often too late to intervene with maintenance. Sensible sensors are often added to have this type of condition monitoring done online (in real time), but most of the time these sensors are expensive and need rigorous inspection. For applications in which the transmission is attached to an electrical generator, the analysis of the absorbed power signal's fluctuations gives information about the presence of a defect, but not about its location in the transmission. [PERSON] et al. [9] employed piezoelectric sensors and optical fibers to detect dynamic strain at specific points on gears with various type of defects. In this study, it was also stated that strain analysis has to be preferred to vibration analysis, because the latter suffers from errors due to the wave's propagation paths, hence a more direct contact between the faulty part and the sensor is preferred. Indeed, while vibration analyses are often not capable of detecting incipient damages due to noises, they are the easiest to set up. The advantages of transmission health monitoring shift the ideology of maintenance from \"check at fixed intervals\" to \"always check and repair at the same time.\" By acting at the right time, major failures, which can cause breakdowns and hence downtimes, could be avoided, guaranteeing the expected working life. This translates to minimum and efficient spending for maintenance, which lowers the overall economical effort of keeping machinery running. This is particularly true for big/complex systems such as wind-turbine gearboxes, which on one hand are very complex and expensive, while the other hand are often placed in areas that are difficult to access. While SHM is useful especially for very complex systems, for such configurations an experimentally based training of the SHM algorithm allows the inclusion of realistic sources of uncertainty retrieved in the field, but it is constrained by the huge costs of the prototypes, especially as the experimental replication of all the potential damages and faults is inconceivable.