Application to induction motor faults diagnosis of the amplitude recovery method combined with FFT

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

“…Fault diagnosis via the current analysis in the frequency domain has become a common method for machine condition evaluation because it is non-invasive, it requires a single current sensor, either a current transformer, a Hall sensor, or a magnetoelectric current sensor [4], and it can identify a wide variety of machine faults [5,6]. Traditionally, these techniques, known as motor current signature analysis (MCSA), have focused on the detection of faults during the steady state functioning of the machine through the current spectrum, which can be computed using the fast Fourier transform (FFT) [7][8][9][10]. For example, bar breakages in the rotor cage produce components of frequencies f bb [9,[11][12][13][14][15][16] f bb = |(1 ± 2ks)| f supply k = 1, 2, 3 .…”

“…where T is half of the total duration and B is the positive bandwidth (in rad/s), equal to half of the total bandwidth. Using (10) and 12, a time-limited signal y(t) can be expanded in terms of the FT of the functions ϕ…”

Fault diagnosis of rotating machinery based on time-frequency decomposition and envelope spectrum analysis

“…Among them, time analysis is the simplest and easiest type for fault diagnosis; however, the test results are neither perfect nor reliable since statistic indicators such as kurtosis, root mean square and mean are used and can be disturbed by noise. Frequency analysis methods mainly use Fourier transform based algorithms to observe mechanical fault characteristic frequency compositions, such as fast Fourier transform (FFT) [4], interpolated FFT [5] and interpolated discrete Fourier transform [6]. However, these traditional frequency methods are based on an assumption that the processed signals are stationary and linear.…”

Analysis of fault signatures for the diagnosis of induction motors fed by voltage source inverters using ANOVA and additive models

“…This technique is based on Motor Current Signature Analysis (MCSA) [9], which is currently considered as a standard in preventive maintenance basically due to the advantages of using non-invasive sensors [10]. More precisely, it was developed an expert system that makes use of the Fast Fourier Transform to obtain the spectrum of the motor line current [11,12] and a multiresolution technique using wavelet functions [13][14][15][16] to detect significant peaks in the spectrum and to measure the height of these peaks with respect to the ''baseline" signal. Finally, a Quality Control approach based on robust multivariate control charts is applied to detect a progressive deterioration of the rotor cage [17,18].…”