# A Review on Vibration-Based Condition Monitoring of Rotating Machinery
## 4 Analytical Review
### Category p2: Features, Signal Processing  
The analysis of the condition of a machine is a complex activity often aimed at a specific purpose. Therefore, there is a wide range of methods for signal processing and features extraction that can be classified into three broad categories: time domain; frequency domain; time-frequency domain; time-frequency domain. In the time domain analysis, the waveform of the signal is used, i.e., its evolution over time, and from this we derive statistic quantities such as mean, peak, peak-to-peak interval, variance, crest factor, as well as high-order statistics like root mean square (RMS), skewness, and kurtosis. However, it may be useful to directly identify certain components of the signal, known as related to the phenomenon being investigated. In this case, frequency domain analysis is necessary, and different order methods have been developed for this purpose. Yet, frequency domain methods of analysis cannot handle non-stationary waveform signals, as normally occurs in presence of deterioration or faults. Based on the stationarity assumption and, therefore, providing statistical averages, they are not suitable for fully describing signals whose statistics vary over time. It is necessary to describe these signals, not only in frequency, but in time as well, and this requires specific methods capable of revealing the local features in both time and frequency domains simultaneously, as in the time-frequency analysis. Moreover, when it comes to extracting information from a signal, signal pre-processing and/or processing is often required to make that extraction faster and more accurate by, for example, filtration, denoising, and/or demodulation. In class p1, documents specifically oriented to signal pre-processing techniques were evaluated, whereas in class p2, examined in this paragraph, works dealing with signal processing and features were considered, with all limitations deriving from the fact that the boundaries between pre-processing and processing are sometimes blurry. A taxonomy of the main studied and applied methods for signal processing and features extraction is proposed in Figure 9; the classification drew inspiration from a previous work by [PERSON] et al. [100], but freely evolved according the needs revealed by the current dataset of documents. Table 3 reports the classification of the articles of class p2, according to the proposed taxonomy. In the adopted classification, the three fundamental domains (time, frequency, time-frequency) have been preserved, including not only the methods used to extract the features, but also those that treat the signal in order to make it suitable, or more suitable, for the extraction of features. In addition to the classes of the three fundamental domains, two more categories account for specific cases that do not fall within, or hardly fall within, the canonical ones: hybrid includes cases where methods belonging to more than one domain are used; unique, on the other hand, includes cases in which new or specially developed methods are used for the specificity of the object of study; comparison collects instead works specifically oriented to the comparison between different methods. Figure 9: Taxonomy of the most common signal processing and feature extraction methods. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Investigied Aspect** & **Baseline** & **Shifts** & **Gener/Genbuses** & **Madons** & **Purpose** & **Wind Turbions** & **General** & **Others** \\
From the classification of the p2 papers of Table 3, iy emerged that frequency domain (spectral methods) and time-frequency domain methods are the more applied ones, while the techniques based only on time domain have a limited application. The largest number of works refers to time-frequency domain methods. In [103], [PERSON] et al. compare the time domain features peak-to-peak amplitude, peak amplitude, and RMS amplitude for the CM of rolling bearings, considering different loads and speeds.