Recursive Undecimated Wavelet Packet Transform and DAG SVM for Induction Motor Diagnosis

Abstract

This paper is focused into the design of a new approach dedicated to solve classification problems for the detection of Broken Rotor Bar (BRB) fault in induction motors. This new method finds its origins in a novel combination of both, recursive undecimated wavelet packet transform (RUWPT) and directed acyclic graph support vector machines (DAG SVM). Most often, BRB frequency components are hardly detected in the stator current due to its low magnitude and its closeness to the supply frequency component. To overcome this drawback, the RUWPT is applied to extract one parameter able to detect the fault with arbitrary working conditions and a great concern of low load cases. Different multi-class support vector machines (MSVM) methods are evaluated with respect to accuracy, number of support vectors, and testing time. The experimental results confirm that the DAG SVM and symlet wavelet kernel function are fast, robust and give the best classification accuracy of 99%. ).A. Braham is with University of Carthage and the MMA research laboratory, Tunisia

Broken Bar Diagnosis for Squirrel Cage Induction Motors Using Frequency Analysis Based on MCSA and Continuous Wavelet Transform

“…The results presented in [45] reduced the computational cost and confirm the amplitude increase at 0-60 Hz frequency levels. Moreover, the Discrete Wavelet Transform (DWT) and the WPT were used together in [19,46] to quantify the amount of energy in low frequencies detail coefficients, which identified the frequency ranges of harmonic components induced in the stator [47,48]. Furthermore, different mother wavelets were applied in the fault diagnosis procedure.…”

“…The Hilbert transform retains the positive content of the original signal frequency while eliminating negative frequencies and increasing by two the DC component. However, current and voltage characterization using both techniques is difficult; the frequency resolution forces the addition of dedicated processes where automatic learning algorithms are executed to identify slight differences between signals [19,20], which increases the software and hardware complexity. The Hilbert transform is used as a complementary tool to the Fourier transform (e.g., to study frequencies that occur instantaneously).…”

“…Based on the wavelet properties, different types of mother wavelets can be used to perform the analysis [25][26][27]. The wavelet transform is considered a useful tool for applications where signal feature extraction is required (e.g., pattern recognition, machine learning, and classifiers) [19,20,[28][29][30][31].…”

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

“…The feed-forward neural network using Levenberg-Marquardt algorithm showed a new way to detect and diagnose induction machine faults [12], where the results were not affected by the load condition and the fault types. In another study, a special structure of support vector machine (SVM) was proposed, which combined Directed Acyclic Graph-Support Vector Machine (DAG-SVM) with recursive undecimated wavelet packet transform, for inspection of broken rotor bar fault in induction motors [13]. Fuzzy system and Bayesian theory were utilized in machine health monitoring in Ref.…”

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

“…In [25,26] the empirical mode decomposition (EMD) is used to extract the fault components. In [27] the recursive undecimated wavelet packet transform (RUWPT) is used to isolate and to compute the RMS value of the components produced by a broken bar fault, using an extremely low sampling frequency (224 Hz) and a small number of current samples (1024 samples). 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.…”