# A Review on Vibration-Based Condition Monitoring of Rotating Machinery
## 4 Analytical Review
### Category p1: Signal Type, Sensors, Pre-Processing
in [67] present a wireless sensor network (WSN) with low-cost nodes formed by microelectromechanical system (MEMS) accelerometers and a highly integrated microcontroller with built-in antenna for Wi-Fi and Bluetooth low-energy (BLE). Another interesting issue is related to the adoption of wireless sensor networks (WSNs). [PERSON] et al. in [68] investigate the under-sampled vibration signals acquisition from a WSN for motor bearings CM. The proposed solution is particularly suitable for installations in remote areas, such as wind farms and offshore platforms. A further work related to WSNs is presented by [PERSON] et al. in [69]. A FPGA-based wireless sensor node was developed, based on Xilinx Artix-7 XC7A35T FPGA circuit, which obtained minimal synchronization error of 60 ns. A significant number of papers deal with pre-processing methods. The denoising is a common problem due to the fact that vibration signals are noisy in nature. [PERSON] and [PERSON] in [70] compare three adaptive noise cancellation (ANC) techniques on the vibration signal acquired from the experimental set-up; [PERSON] et al. in [71] present data denoising by synthesizing the time-frequency manifold (TFM), using time-frequency synthesis and phase space reconstruction (PSR) synthesis; [PERSON] et al. in [73], introduce a novel modified generalized notch filter, used for harmonic vibration generation with an algorithm that automatically gets the rotor desired harmonic vibration; [PERSON] et al. in [74] use a tracking notch filters based on N-channel structures, using the iterative-integrating converters for early diagnosis of bearings under adverse application conditions. [PERSON] et al. in [75] propose a method to detect gear fault by considering the sideband symmetry features around resonance, obtained through the frequency amplitude modulation and frequency modulation (AM-FM) process and the explicit time-varying Fourier spectra under non-stationary conditions obtaining. The demodulation issue is addressed by [PERSON] et al. in [76], who discuss the impact of a limited spectral bandwidth filtering with Hilbert demodulation, evaluating the interactions between the amplitude and phase estimations. [PERSON] et al. in [77] propose an optimization-based demodulation transform based on an optimal demodulation operator (DO), which allows to transform the time-varying frequency component into a constant one so that the largest peak can be detected in the spectrum of the demodulated signal. This method is used for the rolling bearing fault characteristic frequency (FCF) estimation for diagnostic aims. [PERSON] and [PERSON] in [81], similar to [PERSON] et al. [80], presented a method based on continuous vibration separation (CVS), to separate dynamic responses of planet gear from overall vibration responses of planetary gearboxes, and a minimum entropy deconvolution (MED), to enhance the detection of fault-related impulses. A non-parametric blind spectral processing is treated by [PERSON] et al. in [83] for simultaneously denoising and extracting the harmonic content from non-stationary vibration signals. The time signals related to the extracted time-varying harmonic and residual components can be reconstructed; thus, it is a fully invertible technique. [PERSON] et al., in [82], derive blind filter formulations to obtain the most sparse envelope spectrum when filtering a signal, without knowing the characteristic fault frequencies of the mechanical components, and demonstrated the method effectiveness in tracking faults with a cyclostationary signature. The decomposition issue was studied by several researchers and for different components diagnostics; e.g., [PERSON] et al. in [84] present an iterated SVD (ISVD)-based in-band noise reduction method combined with envelope order spectrum analysis, which can extract the fault characteristic order under variable speed conditions.