Development of Enhanced Wigner–ville Distribution Function

Bearing defect detection and diagnosis using a time encoded signal processing and pattern recognition method

“…Second, the uncertainty principle means that good resolutions in both time and frequency-domains cannot be achieved simultaneously [9]. For example, if the signal to be analyzed is of short duration, obviously a narrow window should be selected, but the narrower the window the wider the associated frequency band and the poorer the frequency resolution.…”

Bearing Fault Classification based on the Adaptive Orthogonal Transform Method

“…Vibration signal analysis is one of the most efficient techniques thanks to the useful information to severity and type of bearing damage [3], [4]. Various signal processing techniques have been proposed for mechanical fault diagnosis are time domain [5], frequency domain [6]- [8], time-frequency domain analysis [9], high frequency resonance technique (HFRT) [10], [11], wavelet transform methods [12], [13] and automatic diagnosis techniques [14]. In summary, such methods can be primarily categorized into two classes: frequency identification and features classification.…”

Fault Diagnosis of Induction Machines in Transient Regime Using Current Sensors with an Optimized Slepian Window

“…Other approaches rely on tracking the evolution of the fault harmonics in the time-frequency domain, looking for characteristic patterns of each type of fault, as indicated by (1), (2) and (3); this technique allows the detection of different types of faults, even in the case of mixed faults, with the instantaneous presence of two faults, such as broken rotor bars in the presence of the intrinsic static eccentricity; as [28] states, rotor bars breakage causes the static eccentricity and it is possible that two faults occur simultaneously. TMCSA techniques have been developed in the technical literature using different time-frequency (TF) signal analysis tools [9,29], such as the discrete wavelet transform (DWT) [15,[30][31][32][33][34][35][36], the discrete wavelet packet transform (DWPT) [37], the discrete harmonic wavelet transform (DHWT) [38], the continuous wavelet transform (CWT) [39,40], the complex CWT [41,42], and the Wigner-Ville distribution (WVD) [43,44], among others. Wavelet-based transforms require a proper choice of the mother wavelet and a precise adjustment of the sampling frequency and the number of bands of the decomposition to perform fault diagnosis.…”

“…Quadratic-based transforms, such as the WVD, have, as main drawback, the appearance of the cross-terms effects that can smear the spectrogram of the current signal. The minimization of cross-terms effects has been widely discussed in the technical literature [43][44][45][46][47]. However, in the case of the STFT [44,48], which can be considered the natural extension of FFT-based MCSA techniques, the cross-terms effects do not appear, as the STFT is a linear transform.…”