**Acknowledgments**

The last experiment assessed the damaged bearing with 3.0 mm hole. In this case, it is important to observe a change in the envelope spectrum graphic scale (**Figure 6**), due to the increase

The obtained results validate the methodology, and therefore, the involved theoretical concepts. A BFPO frequency (at 88.1 Hz) was detected for each damaged bearing experiment, strongly indicating a bearing outer race fault. Besides, the characteristic frequency amplitude increases with the fault severity, which could be used as a prognosis indication. In the envelope spectra, it was also observed that as the amplitude of BPFO increased, the amplitude of another frequency component decreased. Thus, as in Ref. [11], it is possible to conclude that, although the stator current analysis is more complex than the vibration analysis, it is an important alternative to bearing fault detection in induction motors, mainly due to its advan-

**Figure 6.** Squared envelope spectrum of the electric current for the damaged bearing with the 3.0 mm hole.

This work describes a methodology to enhance MCSA for bearing fault detection and identification in induction machines by combining electrical currents sum, prewhitening based on linear

tages related to cost, availability and applications.

**5. Conclusions and comments**

in amplitude (*A* = 11.3 × 10− 9) in the observed fault frequency.

The authors would like to thank the National Council for Scientific and Technological Development (CNPq), Coordination for the Improvement of Higher Education Personnel (CAPES), Minas Gerais Research Foundation (FAPEMIG), and Brazilian Electricity Regulatory Agency Research and Development (ANEEL R&D) for supporting this work.

The authors would like to thank the Professor J. Antoni for providing the Fast Kurtogram Matlab® code.
