# A Review on Vibration-Based Condition Monitoring of Rotating Machinery
## 4 Analytical Review
### Category p2: Features, Signal Processing
From the study, it emerged that peak-to-peak amplitude gives better results, followed by peak amplitude and RMS amplitude, and, in all considered conditions, any defect type is best detected by adopting peak-to-peak amplitude as the feature. [PERSON] and [PERSON], in [105], present a time-domain diagnostic algorithm, to address the issue of the difficult modulated sideband extraction through spectral analyses techniques. The proposed method combines the fast dynamic time warping (fast DTW) as well as the correlated kurtosis (CK) techniques to characterize the local gear fault, and to identify the corresponding faulty gear and its position. This technique is beneficial in a practical analysis to highlight sideband patterns in situations where data are often contaminated by process/measurement noises and small fluctuations in operating speeds that occur, even at otherwise presumed steady-state conditions. The temporal collocation of the papers classified in the class of time-based methods, compared with those of the works that are placed in the other classes, reveals a reduction of research interest in recent years regarding these techniques. Spectral methods are mainly used for diagnostics of bearings and gearboxes, but this trend also applies to the other methods. With reference to first order spectral methods, [PERSON] et al. in [123] adopt the frequency and phase spectral analysis to diagnose the bearings vibrations root cause and found that phases are useful to detect misalignments, being related to relative motion between parts. [PERSON], in [118], presents a method based on spectral analysis to automatically identify the amplitude level for signal decomposition, obtaining results in identifying bearing damages at the early stage of development better than with the classic vibration analysis. [PERSON] and [PERSON] in [117] developed the whitened cross-correlation spectrum (WCCS) method based on the cross-correlation between the whitened vibration signal and its envelope, with good performances in the bearings damages early detection. In [119], [PERSON] et al. generate two-dimensional (2D) images, starting from the signal FFT-based spectrum, reduce the images dimension through the two-dimensional principal component analysis (2 DPCA), and finally apply a k-nearest neighbor method to classify bearing faults. [PERSON] et al., in [121], verified that localized and distributed faults could be distinguished by comparing envelope spectra of vibration signals. [PERSON] et al., in [125], developed a vibration waveform frequency spectrum analysis method to get the fault severity, and to analyze the cause of the problem for wind turbine gearboxes. [PERSON] and [PERSON], in [124], proposed cepstral analysis to detect fatigue cracks in machines through analysis the autocorrelation function of a filtrated cepstrum, which enables detecting the initiation of a fatigue crack. The depth of the fatigue crack can be easily determined with the help of the number and amplitude of harmonics in the autocorrelation function, and their relative distance. In [129], [PERSON] and [PERSON] presented a new method to construct a single composite spectrum using all the measured vibration data set, whose performances were verified on data achieved on a laboratory test rig. Results of the method applied with the spectrum, with and without the coherence, have been investigated for the simulated faults in the rig, demonstrating that the coherent composite spectrum provides a much better diagnosis compared to the non-coherent composite spectrum. Furthermore, the composite spectrum represents the dynamics of the complete machine assembly and can make the fault diagnosis process relatively easier and more straightforward. Time-Frequency methods are widely investigated and adopted for bearings and gears and some applications to motors have been found. For bearings diagnostics, [PERSON] et al.