Title :
Fault Diagnosis in Industrial Induction Machines Through Discrete Wavelet Transform
Author :
Bouzida, A. ; Touhami, O. ; Ibtiouen, R. ; Belouchrani, A. ; Fadel, M. ; Rezzoug, A.
Author_Institution :
Ecole Nat. Polytech., Algiers, Algeria
Abstract :
This paper deals with fault diagnosis of induction machines based on the discrete wavelet transform. By using the wavelet decomposition, the information on the health of a system can be extracted from a signal over a wide range of frequencies. This analysis is performed in both time and frequency domains. The Daubechies wavelet is selected for the analysis of the stator current. Wavelet components appear to be useful for detecting different electrical faults. In this paper, we will study the problem of broken rotor bars, end-ring segment, and loss of stator phase during operation.
Keywords :
asynchronous machines; discrete wavelet transforms; fault diagnosis; frequency-domain analysis; rotors; stators; time-domain analysis; Daubechies wavelet; discrete wavelet transform; fault diagnosis; frequency domains; industrial induction machines; rotor bars; stator current; time domains; wavelet components; wavelet decomposition; Approximation methods; Induction machines; Rotors; Stator windings; Wavelet transforms; Broken rotor bars; data-dependent selection (DDS) and data-independent selection (DIS) of the decomposition level; fault diagnosis; induction machines (IMs); motor-current signature analysis (MCSA); wavelet transform;
Journal_Title :
Industrial Electronics, IEEE Transactions on
DOI :
10.1109/TIE.2010.2095391