@article{scholars9568,
           title = {Unsupervised on-line method to diagnose unbalanced voltage in three-phase induction motor},
          number = {12},
            note = {cited By 4},
          volume = {30},
             doi = {10.1007/s00521-017-2973-0},
         journal = {Neural Computing and Applications},
       publisher = {Springer London},
           pages = {3877--3892},
            year = {2018},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017624057&doi=10.1007\%2fs00521-017-2973-0&partnerID=40&md5=01c3424c5b16225781cf6fc1efeb8091},
        keywords = {Electric fault currents; Failure analysis; Fault detection; Harmonic analysis; Neural networks; Stators, Analytical expressions; Classification methods; Feed-forward multilayer perceptron; Statistical parameters; Stator currents; Three phase induction motor; Unsupervised; Winding function, Induction motors},
        abstract = {In this paper, an unsupervised automatic method based on a current signature neural network (NN) is presented to on-line diagnose stator fault without the inspection of any supervisor or technician. To extract the fault regime, the knowledge of current signature will not be enough; therefore, mathematical model, numerical analysis, as well as artificial intelligence (AI) are taken into account to extract the exact unbalanced voltage stator fault. Analytical expressions are derived for a stator conductor segment in order to find out the conductors that are responsible for the generation of magnetomotive force (MMF). A test rig is designed using three-phase induction motor, two-axis PASPORT sensor, PC, and PASCO interface to compute the effect of MMF at the stator side through a new series of harmonics which are helpful to tackle the scrupulous effect of an unbalanced voltage at the incipient stage. Further, an unsupervised NN has been introduced that endeavors the principal components of the new series of harmonics. The statistical parameters of a new series of harmonics are contemplated as input features for NN that not only diagnose unbalanced voltage but also identify the degree of unbalanced voltage through feed-forward multilayer perceptron (MLP) trained by backpropagation. The validation and performance of proposed methods have been theoretically and experimentally analyzed on a custom-designed test rig under various levels of unbalanced voltage. Moreover, the NN classification method shows higher accuracy with enough robustness to various levels of unbalanced voltage, which states that the proposed method is suitable for the real-world applications. {\^A}{\copyright} 2017, The Natural Computing Applications Forum.},
          author = {Sheikh, M. A. and Nor, N. M. and Ibrahim, T. and Irfan, M.},
            issn = {09410643}
}