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Abstract
Induction motors are one of the most widely used electrical machines. Statistics of bearing failures of induction motors indicate, that they constitute more than 40% of induction motor damage. Therefore, bearing diagnosis is so important for trouble-free work of induction motors. The most common methods of bearing diagnosis are based on vibration signal analysis. The main disadvantage of those methods is the need for physical access to the diagnosed machine, which is not always possible. Methods based on motor current signature analysis are free of this disadvantage. Preliminary studies have shown that motor current signature analysis based normalized triple covariance is a very good diagnostic indicator for induction motor bearings. This paper presents an attempt to find a more accurate diagnostic indicator based on normalized triple covariance. In this paper the author verifies how many diagnostic features (normalized triple covariances) included in diagnostic indicator can give better separation between healthy and unhealthy cases.
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- Publication version
- Accepted or Published Version
- License
- Copyright (2017 IEEE)
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) strony 498 - 502
- Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- Ciszewski T.: Induction motor bearings diagnostic indicators based on MCSA and normalized triple covariance// 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)/ : , 2017, s.498-502
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/demped.2017.8062401
- Verified by:
- Gdańsk University of Technology
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