In the frequency domain, [PERSON] et al. [10] studied bearing monitoring by applying the Discrete Fourier Transform (DFT) to sample signals from wind turbines, while [PERSON] et al. [11] proposed a new spectrogram called a Fault Energy of Correntropy diagram that can detect bearing fault frequencies with high precision. This new technique was validated both through simulations and experimental tests. A DFT-based method to monitor a gear transmission was proposed in [12]. In the time-frequency domain, [PERSON] et al. [13] used techniques that transform signals from the time domain to the time-frequency domain, such as the Short-Time Fourier Transform (STFT) and the Wigner-Ville distribution to identify variations in vibrational behavior between a healthy and a damaged planetary gear under different loading conditions. A STFT method to identify gear whine noise using microphones was reported in [14]. [PERSON] et al. [15] defined an index to detect the cavitation phenomenon in a spoo valve. Research efforts have been strongly focused on the ML field. [PERSON] et al. [16] presented a supervised ML classification model comparison, finding that the best model for monitoring an axial piston pump health was the Decision Tree (DT) model. The analysis involved calculating a few features on a raw signal, the signal in the angular domain, and its DFT, which were carefully selected after Principal Component Analysis (PCA). [PERSON] et al. [17] developed an Artificial Neural Network (ANN) classifier to identify bearing defect types with input features derived from vibrational signals following careful selection through a Genetic Algorithm. An interesting development was a DT model for automotive transmission health monitoring [18]. The authors derived features from vibrational signals in both the time and frequency domains, noting that the best model was the tree trained solely with features from the time domain. [PERSON] et al. [19; 20] proposed a Neuro-Fuzzy logic algorithm for the health monitoring of different gear types. [PERSON] et al. [21] developed new indicators derived from vibrational signal spectral analysis used to train supervised ML classification models. An Adaptive AR model was proposed in [22] using vibrational signals previously filtered with an Adaptive Kalman Filter (KF) that showed more reliable results compared with other filtering techniques such as TSA. [PERSON] et al. [23] proposed a time-varying AR model with the help of an Extended KF based on residual signals from a gear mesh under healthy conditions for the online monitoring of gearbox vibrations. The model order selection was carried out through hypothesis testing: the developed model was then experimentally tested, showing better results in detecting incipient faults in a gearbox compared with classical analysis techniques but also showing a higher number of false alarms. [PERSON] et al. [24] developed a Non-Linear AR model based on an ANN to simulate pump behavior for monitoring cavitation phenomena: the model outputs were analyzed in both the time and frequency domains, demonstrating reliable results. Among the most innovative techniques, [PERSON] et al. [25] reviewed works aimed at monitoring gear wear through vibrational analysis, identifying that current highly regarded techniques significantly focus on the development of digital twins. Innovative methodologies based on ML models were reported in [26; 27]. [PERSON] et al. [28] developed a new technique, Supervised Order Tracking Bounded Component Analysis, demonstrating excellent results in detecting gear cracks in simulations. [PERSON] et al. [29] proposed a new technique combining dynamic models, tribological models, and both simulated and real signals to monitor the surface wear condition of contacting gears. Other techniques involve combining vibrational and acoustic signals [30; 31; 32] or vibrational and current signals [33; 34]. [PERSON] et al. [35] proposed a sensor fusion approach to detect faults in an Internal Combustion Engine (ICE). [PERSON] et al.