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Multiple-fault diagnosis in induction motors through support vector machine classification at variable operating conditions

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Abstract

This work presents a fault diagnosis strategy for induction motors based on multi-class classification through support vector machines (SVM), and the so-called one-against-one method. The proposed approach classifies four different motor conditions (healthy, misalignment, unbalanced rotor and bearing damage) at variable operating conditions (supply frequency and load torque). The proposed SVMs use signatures from the frequency domain characteristics related to each studied fault. These signatures combine information just from the stator condition: radial vibration and stator currents. To acquire training and validation data in steady state, different experiments were performed using a three-phase induction motor. Thirty-five data sets were obtained at different operating regimes of the induction motor for each specific fault (140 conditions including a no-fault scenario) to validate our study. The SVMs with a Gaussian radial basis function (RBF) were proposed as a kernel for the nonlinear classification process. To select the parameter value of the RBF, a bootstrap technique was used. The resulting accuracy for the fault classification process was on the range 84.8–100%.

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Authors and Affiliations

  1. Facultad de Ingenieria, Universidad Autonoma de San Luis Potosi, San Luis Potosí, Mexico

    José D. Martínez-Morales

  2. Facultad de Ciencias, Universidad Autonoma de San Luis Potosi, San Luis Potosí, Mexico

    Elvia R. Palacios-Hernández & D. U. Campos-Delgado

Authors
  1. José D. Martínez-Morales
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  2. Elvia R. Palacios-Hernández
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  3. D. U. Campos-Delgado
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Corresponding author

Correspondence to D. U. Campos-Delgado.

Additional information

This research was supported in part by the Universidad Autonoma de San Luis Potosi through an FAI grant.

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Martínez-Morales, J.D., Palacios-Hernández, E.R. & Campos-Delgado, D.U. Multiple-fault diagnosis in induction motors through support vector machine classification at variable operating conditions. Electr Eng 100, 59–73 (2018). https://doi.org/10.1007/s00202-016-0487-x

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  • DOI: https://doi.org/10.1007/s00202-016-0487-x

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