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Gain Property and Data Analysis for Diagnosing Failures in a High-Efficiency Induction Motor

Published: 26 February 2023 Publication History

Abstract

Induction motors are the most commonly used in the industrial market, corresponding to 90% in areas such as manufacturing, pharmaceutical, machines and tools; this is due to its robustness compared to other types of machines. Due to the main role they play in large scale production, they should not stop due to failures. From this perspective, it is intended to diagnose any type of malfunction that occurs in these traction machines, before a production stop takes place. These situations give rise to the proposition of a variety of time-domain and frequency-domain methods to make a successful diagnosis of the failures. This paper proposes the Gain Property method, which relates the currents and voltages (C/V) supplied to a high-efficiency induction motor; the results obtained by such method are stated in two ways: using statistical tools (gray correlation, average deviation and quadratic deviation) and a Bayesian probabilistic tool, in order to analyze the behavior of the results and obtain a favorable diagnosis. In a testbench the motor was subject to four types of incipient failures, and after processing the data of the gains in the three supply lines it was concluded that, depending on the techniques used as statistical tool, the effectiveness of the diagnosis changes, approximating its results in 35%; on the other hand, the Bayesian probabilistic method exhibited a significant improvement for failure diagnosis.

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cover image ACM Other conferences
ICISE '22: Proceedings of the 7th International Conference on Information Systems Engineering
November 2022
86 pages
ISBN:9781450397889
DOI:10.1145/3573926
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 26 February 2023

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Author Tags

  1. Failure Diagnosis
  2. Gain Property
  3. Induction Motor
  4. Statistical Analysis

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  • Research-article
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  • Refereed limited

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  • Universidad Politécnica Salesiana

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ICISE 2022

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