Transient Current analysis

The Research Team has a long experience in the development of advanced techniques based on the analysis of motor transient currents (and more, specifically, on the analysis of the current demanded by the motor under starting).

The classical current based methods (such as Motor Current Signature Analysis, MCSA) rely on the analysis of the current demanded by the motor at steady-state operation by applying the Fourier transform. The idea is to evaluate the amplitude of certain harmonics that are amplified when the corresponding fault is present.

On the contrary, the new techniques developed by the group (named ATCSA- Advanced Transient Current Signature Analysis) are based on the analysis of the current demanded by the machine during transient operation (e.g. under starting). The idea here is to analyse that transient current signal by applying sophisticated time-frequency tools with the aim of tracking the evolutions caused by the fault harmonics during the transient; whereas at steady-state these fault harmonics fall at specific frequencies, during the transient regime, as the slip varies, the frequencies of these harmonics change over time, yielding characteristic time-frequency evolutions that, if properly identified, can be used as reliable indicators of the presence of the fault.

The advantages of the ATCSA approach, based on the identification of time-frequency evolutions of fault harmonics during transient, are notorious and have been proven in many publications in which the Team has taken part. Some of these advantages are detailed next:

- ATCSA has higher reliability than classical methods (since, unlike the fault frequencies in the FFT spectra that can be partially masked by phenomena that are not related to the failure, the time-frequency patterns can be barely provoked by a phenomena that is not related to the fault).

- ATCSA enables to avoid false indications of classical methods: this has been proven in cases such as presence of load torque oscillations, existence of rotor cooling ducts, diagnosis of unloaded machines, detection of broken outer bars in double cage rotors…

- Simpler discrimination between different faults; when simultaneous faults are present in the machine, the patterns caused by the transient evolutions of different fault frequency components are often better identified and separated than the frequency peaks in the Fourier spectrum which may overlap at certain frequencies.

- Possibility of tracking multiple components evolutions: the approach does not only rely on tracking a single component but on identifying the multiple evolutions caused by fault harmonics during transients. This aspect increments the reliability in the diagnostic.

- ATCSA does not require additional measurement equipment in comparison with classical current-based approaches: the same current clamp and oscilloscope may be used and the measurement can be carried out at the same point (e.g. secondaries of the C.Ts). Moreover, required sampling rate is similar than that used in MCSA.

- ATCSA can be applied to multiple transient regimes (starting, load variations, plugging stop,…), different types of electric motors (cage induction motors, wound rotor induction motors, synchronous motors…) as well as to motor driven by different types of drives (line-fed, soft-started motors, inverter-fed…).

The Team has developed, during more than 15 years, especial advanced tools that have been optimized for the application of ATCSA to detect different types of faults. As a result, the Team has implemented some software tools, such as A-CSA, so that industrial users can purchase a license for their application (see ‘software’ section of the website.

Nowadays, the ongoing research in the line of transient current analysis is focused on the following aspects:

- Optimization of the ATCSA methods and adaptation to specific applications (e.g. traction systems).

- Search of novel time-frequency methods that may enhance the performance of the existing ones for certain applications.

- Application and adaptation of ATCSA techniques to other types of machines (e.g. PM motors).

- Enhancement of the automation in the diagnostic (i.e. without user intervention) by using advanced artificial intelligence and pattern recognition tools.

- Implementation of ATCSA methods in smart sensors.

- Integration of ATCSA and other techniques.

Representative team projects: MOTORTECH, SISTINMOT, DIMER

Most relevant publications:

1. J. Antonino-Daviu, M. Riera-Guasp, J. Roger-Folch and M.P. Molina, “Validation of a New Method for the Diagnosis of Rotor bar Failures via Wavelet Transformation in Industrial Induction Machines,” IEEE Transactions on Industry Applications, Vol. 42, No. 4, , pp. 990-996, July/August 2006.

2. J. Antonino-Daviu, P. Jover, M. Riera-Guasp, J. Roger-Folch and A. Arkkio, “DWT Analysis of Numerical and Experimental Data for the Diagnosis of Dynamic Eccentricities in Induction Motors”, Mechanical Systems and Signal Processing, Elsevier, vol. 21, no. 6, pp. 2575-2589, August 2007.

3. Jose Antonino-Daviu, Martin Riera-Guasp, Joan Pons-Llinares, Jongbin Park, Sang Bin Lee, Jiyoon Yoo and Christian Kral, “Detection of Broken Outer Cage Bars for Double Cage Induction Motors under the Startup Transient”, IEEE Transactions on Industry Applications, vol. 48, no.5, pp. 1539-1548, Sept-Oct. 2012.

4. J. Antonino-Daviu, S. Aviyente, E. G. Strangas and M. Riera-Guasp, "Scale Invariant Feature Extraction Algorithm for the Automatic Diagnosis of Rotor Asymmetries in Induction Motors," in IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 100-108, Feb. 2013.

5. J. Pons-Llinares, J. Antonino-Daviu, J. Roger-Folch, D. Moríñigo-Sotelo, O. Duque-Pérez, “Mixed eccentricity diagnosis in Inverter-Fed Induction Motors via the Adaptive Slope Transform of transient stator currents,” Mechanical Systems and Signal Processing, vol. 48, no.1-2, pp. 426-435, Oct. 2014.

6. C. Yang, T. Kang, D. Hyun, S.B. Lee, J. Antonino-Daviu, J. Pons-Llinares, "Reliable Detection of Induction Motor Rotor Faults Under the Rotor Axial Air Duct Influence," IEEE Transactions on Industry Applications, vol.50, no.4, pp.2493-2502, July/Aug. 2014.

7. J. Corral Hernandez, J. A. Antonino-Daviu, V. Climente-Alarcon, J. Pons-Llinares, V. Frances-Galiana, “Transient-based rotor cage assessment in induction motors operating with soft-starters,” IEEE Transactions on Industry Applications, vol. 51, no.5, pp. 3734-3742, Sep/Oct. 2015.

8. Jose A. Antonino-Daviu, Joan Pons-Llinares, Sang Bin Lee, “Advanced Rotor Fault Diagnosis for Medium-Voltage Induction Motors Via Continuous Transforms”, IEEE Transactions on Industry Applications, vol. 52, no.5, pp. 4503-4509, Sep/Oct. 2016.

9. Y. Park; J. Myung; S. B. Lee; J. A. Antonino-Daviu; M. Teska, "Influence of Blade Pass Frequency Vibrations on MCSA-based Rotor Fault Detection of Induction Motors," IEEE Transactions on Industry Applications , vol. 53, no. 3, pp. 2049-2058, May-June 2017.

10. Jose Antonino-Daviu, Alfredo Quijano-Lopez, Vicente Climente-Alarcon and Carlos Garín-Abellán, “Reliable Detection of Rotor Winding Asymmetries in Wound Rotor Induction Motors via Integral Current Analysis”, IEEE Transactions on Industry Applications, vol. 53, no. 3, pp. 2040-2048, May-June 2017.

11. J. A. Antonino-Daviu, A. Quijano-López, M. Rubbiolo and V. Climente-Alarcon, "Advanced Analysis of Motor Currents for the Diagnosis of the Rotor Condition in Electric Motors Operating in Mining Facilities," in IEEE Transactions on Industry Applications, vol. 54, no. 4, pp. 3934-3942, July-Aug. 2018.

12. Jose Antonino-Daviu, Vicente Fuster-Roig, Sanguk Park, Yonghyun Park, Hanchun Choi, Jongsan Park, Sang Bin Lee, (2020) "Electrical Monitoring of Damper Bar Condition in Salient-Pole Synchronous Motors Without Motor Disassembly," in IEEE Transactions on Industry Applications, vol. 56, no. 2, pp. 1423-1431, March-April 2020.