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Decision fusion scheme for bearing defects diagnosis in induction motors

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Abstract

Intelligent fault diagnostic systems are fast becoming key instruments in industrial applications. This paper presents a recognition system for diagnosing bearing defects in induction motors. The proposed scheme is comprised of five steps, namely signal segmentation, feature extraction and reduction, fault classification and the decision fusion. First, the vibration signal is segmented into successive equal-length intervals, which are considered as patterns in a recognition problem. The objective is to predict the defect mode (class) for each pattern. Then, the time- and the frequency-domain features are extracted from each interval. At the next step, a small set of distinctive and informative features is found by resorting to different feature reduction techniques to guarantee well-organized learning and immediate and accurate classification. Then, in the fourth step, various classifiers are trained to learn to distinguish between the faulty and healthy states. To make the final decision, different combinations of classifiers are considered using the voting and stacking techniques to enhance the overall performance of the recognition system. Evaluation of the proposed diagnostic scheme on the standard CWRU bearing defect database demonstrates that this system attains reasonable performance measures, validating the ideas put forward in this paper.

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Authors and Affiliations

  1. Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

    Hamed Agahi & Azar Mahmoodzadeh

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  1. Hamed Agahi
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  2. Azar Mahmoodzadeh
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Correspondence to Hamed Agahi.

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Agahi, H., Mahmoodzadeh, A. Decision fusion scheme for bearing defects diagnosis in induction motors. Electr Eng 102, 2269–2279 (2020). https://doi.org/10.1007/s00202-020-01024-4

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