Abstract
In this work, a new time-frequency tool based on minimum-norm spectral estimation is introduced for multiple fault detection in induction motors. Several diagnostic techniques are available to identify certain faults in induction machines; however, they generally give acceptable results only for machines operating under stationary conditions. Induction motors rarely operate under stationary conditions as they are constantly affected by load oscillations, speed waves, unbalanced voltages, and other external conditions. To overcome this issue, different time-frequency analysis techniques have been proposed for fault detection in induction motors under non-stationary regimes. However, most of them have low-resolution, low-accuracy or both. The proposed method employs the minimum-norm spectral estimation to provide high frequency resolution and accuracy in the time-frequency domain. This technique exploits the advantages of non-stationary conditions, where mechanical and electrical stresses in the machine are higher than in stationary conditions, improving the detectability of fault components. Numerical simulation and experimental results are provided to validate the effectiveness of the method in starting current analysis of induction motors.
Highlights
Condition monitoring has become a critical issue in rotatory machines, and the induction machines are not an exception
The Min-Norm is compared with two benchmark methods, the fast Fourier transform (FFT) and the multiple signal classification (MUSIC) algorithm
The results reveal that the Min-Norm method achieves a high-frequency resolution and a good estimation accuracy of the amplitudes from relatively small length data
Summary
Condition monitoring has become a critical issue in rotatory machines, and the induction machines are not an exception. Induction motors (IMs) are widely used in industrial systems due to their excellent performance, high robustness and low-cost They are susceptible to many different types of electrical and mechanical faults including, stator winding faults, rotor faults, bearing faults, etc. The monitoring of a steady-state operation can produce false fault alarms due to superposition of different frequency components in the stator current spectrum To overcome this issue, different time-frequency analysis techniques have been proposed for time-varying spectrum analysis, including Fourier-based schemes [6,7,8,9], application of adaptive windows [10], adaptive scales [11], quadratic time-frequency (t, f ) distributions such as the Wigner–Ville distribution [12,13,14], the use of (t, f ) atoms [15], continuous time-frequency
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