Induction machine condition monitoring using notch-filtered motor current


MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol.23, no.8, pp.2658-2670, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 23 Issue: 8
  • Publication Date: 2009
  • Doi Number: 10.1016/j.ymssp.2009.05.011
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2658-2670
  • Keywords: Induction machine, Condition monitoring, Notch-filter, Skewness, Kurtosis, BROKEN ROTOR BAR, FAULT-DETECTION, DIAGNOSIS
  • Anadolu University Affiliated: Yes


This paper presents a new approach to induction motor condition monitoring using notch-filtered motor current signature analysis (NFMCSA). Unlike most of the previous work utilizing motor current signature analysis (MCSA) using spectral methods to extract required features for detecting motor fault conditions, here NFMCSA is performed in time-domain to extract features of energy, sample extrema, and third and fourth cumulants evaluated from data within sliding time window. Six identical three-phase induction motors were used for the experimental verification of the proposed method. One healthy machine was used as a reference, while other five with different synthetic faults were used for condition detection and classification. Extracted features obtained from NFMCSA of all motors were employed in three different and popular classifiers. The proposed motor current analysis and the performance of the features used for fault detection and classification are examined at various motor load levels and it is shown that a successful induction motor condition monitoring system is developed. Developed system is also able to indicate the load level and the type of a fault in multi-dimensional feature space representation. In order to test the generality and applicability of the developed method to other induction motors, data acquired from another healthy induction motor with different number of poles and rated power is also incorporated into the system. In spite of the above difference, the proposed feature set successfully locates the healthy motor within the classification cluster of "healthy motors" on the feature space. (C) 2009 Elsevier Ltd. All rights reserved.