@article{scholars10207,
            year = {2018},
           pages = {5941--5950},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {IEEE Transactions on Industrial Electronics},
             doi = {10.1109/TIE.2017.2782240},
            note = {cited By 36},
          volume = {65},
          number = {7},
           title = {Electrical Signature Analysis-Based Detection of External Bearing Faults in Electromechanical Drivetrains},
        abstract = {An electrical signature analysis (ESA)-based method to detect bearing defects in electromechanical drivetrains (e.g., wind turbines) is proposed in this paper. The novelty of the proposed technique is justified by its capability to detect a defect that occurs in a drivetrain bearing, external to the machine. Unlike the traditional vibration-based method, the electrical measurements of the terminal machine are utilized in this case to perform the fault detection. The proposed ESA-based method is developed utilizing a new electrical signal model of bearing defect, proposed in this paper, based on the excitation of torsional resonances. The proposed ESA-based method and the signal model are validated through experimental tests. The proposed technique demonstrates excellent fault detection capability even at low operating speed, which is advantageous over the traditional vibration-based method in terms of sensor cost, installation complexity, and detection reliability. {\^A}{\copyright} 1982-2012 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038853139&doi=10.1109\%2fTIE.2017.2782240&partnerID=40&md5=3e4f9c48da3bc0b85eb19552ad65962f},
        keywords = {Condition monitoring; Defects; Electric fault currents; Electric variables measurement; Failure analysis; Jitter; Monitoring; Vibration analysis; Wind turbines, Detection capability; Electrical measurement; Electrical signal; Electrical signature analysis; Electromechanical drivetrain; Shafts; Torsional resonances; Vibrations, Fault detection},
          author = {Shahriar, M. R. and Borghesani, P. and Tan, A. C. C.},
            issn = {02780046}
}