# A Review on Vibration-Based Condition Monitoring of Rotating Machinery
## 4 Analytical Review  
### Category p1: Signal Type, Sensors, Pre-Processing  
For the three investigated aspects (signal analysis, sensors, and pre-processing techniques), based on the analysis of the papers, sub-classes related to the mainly investigated themes were identified. Bearings and Gears/gearboxes are the most common topics for the signal aspect, which is in accordance with prospective review results. Three subsections of the signal type aspect refer to the use of a different signal with respect to the vibrations or a combined use. Many articles compare the results obtained by Acoustic Emissions CM (AE-CM) vs. Vibrations-Based CM (VB-CM). For bearings, [PERSON] in 2021 [35] found that AE-CM overcomes VB-CM in terms of fault signal-to-noise ratio, early fault diagnosis, and compound fault diagnosis capabilities; furthermore, due to a high sampling rate, a lower computational efficiency distinguishes AE-CM. [PERSON] et al. in 2017 [36] noted that AE are sensitive to defect size and to rotating speed, otherwise the amplitude of vibrations has no significant changes as the defect size increases and vibration has a poor sensitivity to speed changes. For gearboxes, [PERSON] et al. [37] found through their experimentations--and in agreement with [35]--that sampling rates required by AE-CM are much higher than those required by VB-CM. Furthermore, damage levels can be detected through the AE-CM and not with VB-CM, and AE signals show a stable performance, whereas mechanical resonance easily affects vibration signals (VS). The comparison between vibrations and stator currents-based approaches for CM was investigated in systems with electric motors. As visible in Table 2, some papers related to this issue are not focused on electric motors, because a different component was the object of CM, but the system is based on an electric motor. [PERSON] et al. in 2018 [38] presented the results for a drive train gearbox fault diagnosis and concluded that in vibration signals ghost frequencies are fault sensitive, VS are modulated by shaft rotating frequencies, the fundamental frequency component is dominant in current signals and, at the fundamental frequency, they are modulated by gearbox characteristic frequencies when gear faults appear. [PERSON] in 2015 [39] deduced from the experiments of an automatic condition monitoring for rolling-element bearings--based on vibrations as well as stator current analysis--that vibration analysis had powerful capability in the bearing point and fault severity diagnosis, whereas current analysis showed a moderate capability. [PERSON] et al. in [40] presented a comparison between current and vibration signals for the diagnosis of bearing faults in induction machines and concluded that current signal-based methods are suitable to detect only faults with quite low critical frequency rate, whereas vibration signals are robust indicators for bearing defects. [PERSON] et al., in [41], developed and validated a current-aided vibration order tracking method adopted in variable-speed wind turbine bearing fault diagnosis. Figure 8: Evolution in time of the documents referring to the categories in the taxonomy by topics. Data are presented in stacked format. Black line—the total amount of documents by year. Thermal analysis combined with vibration signals were considered by [PERSON] et al. [50,52] and by [PERSON] et al. [51] for bearing diagnostics. [PERSON] obtained that temperature measurements addicted to the VB-CM model improved fault diagnosis. [PERSON] concluded that using vibrations together with source thermography featuring a good accuracy can be achieved and that plausible diagnostics results may be obtained with the thermography method. Lateral vibration-based measurements and fault diagnosis are highly studied, but both developed and standardized wheatear torsional vibrations-based ones are unusual.