Detection of induction machines rotor faults at standstill using signals injection

The main topic of this paper is to examine the feasibility of detecting the rotor faults of induction machines by performing standstill tests. It has been shown that by feeding the machine with special excitation signals such as discrete interval binary sequence (DIBS) and multisine, it is possible to excite with low-frequency resolution the faulty modes by analyzing the stator current and the stray flux measured by an external flux sensor. This method can be used for quality control just after manufacturing the rotor and mounting it within the stator frame. The proposed technique is fully general and can be applied to a three-phase squirrel-cage induction machine at standstill. Experimental measurements are made both on healthy and faulty machines and the comparison gives a difference in the external flux and stator current signatures which are basically the most commonly used methods for rotor fault detection.

[1]  J. Schoukens,et al.  Peak factor minimization using a time-frequency domain swapping algorithm , 1988 .

[2]  G.E. Dawson,et al.  The detection of broken bars in the cage rotor of an induction machine , 1988, Conference Record of the 1988 IEEE Industry Applications Society Annual Meeting.

[3]  Rik Pintelon,et al.  Identification of Linear Systems: A Practical Guideline to Accurate Modeling , 1991 .

[4]  J. Penman,et al.  Detection and location of interturn short circuits in the stator windings of operating motors , 1994 .

[5]  Rik Pintelon,et al.  Identification of synchronous machines parameters using broadband excitations , 1994 .

[6]  Wojciech Jarzyna.  Diagnostic characteristics of axial flux in an induction machine , 1995 .

[7]  B. Mpanda-Mabwe,et al.  Estimation of synchronous machine parameters from multisine stand-still frequency response test data , 1995 .

[8]  J. L. Kohler,et al.  Condition-based maintenance of electrical machines , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[9]  Hamid A. Toliyat,et al.  Condition monitoring and fault diagnosis of electrical machines-a review , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[10]  S. A. Volokhov,et al.  Method for detection of broken bars in induction motors , 2000 .

[11]  Jim Penman,et al.  Induction machine condition monitoring with higher order spectra , 2000, IEEE Trans. Ind. Electron..

[12]  F. Filippetti,et al.  Quantitative evaluation of induction motor broken bars by means of electrical signature analysis , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).

[13]  H. Henao,et al.  A frequency-domain detection of stator winding faults in induction machines using an external flux sensor , 2002, Conference Record of the 2002 IEEE Industry Applications Conference. 37th IAS Annual Meeting (Cat. No.02CH37344).

[14]  Keith R. Godfrey,et al.  The generation of binary and near-binary pseudorandom signals: an overview , 2002, IEEE Trans. Instrum. Meas..