The Use of Digital Twins in Finite Element for the Study of Induction Motors Faults
| Autor: | Adroaldo Raizer, Wilson Valente Junior, Tiago Drummond Lopes |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
condition monitoring Finite Element Analysis finite element method TP1-1185 Fault (power engineering) Biochemistry Article Analytical Chemistry law.invention non-destructive testing methods law digital twin Industry Computer Simulation Electrical and Electronic Engineering Instrumentation Artificial neural network Rotor (electric) Chemical technology Process (computing) Condition monitoring Control engineering fault diagnosis simulation 3D models three-phase induction motor Atomic and Molecular Physics and Optics Frequency domain Multilayer perceptron Neural Networks Computer Algorithms Induction motor |
| Zdroj: | Sensors, Vol 21, Iss 7833, p 7833 (2021) Sensors; Volume 21; Issue 23; Pages: 7833 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | Induction motors play a key role in the industrial sector. Thus, the correct diagnosis and classification of faults on these machines are important, even in the initial stages of evolution. Such analysis allows for increased productivity, avoids unexpected process interruptions, and prevents damage to machines. Usually, fault diagnosis is carried out by analyzing the characteristic effects caused by the faults. Thus, it is necessary to know and understand the behavior during the operation of the faulty machine. In general, monitoring these characteristics is complex, as it is necessary to acquire signals from the same motor with and without failures for comparison purposes. Whether in an industrial environment or in laboratories, the experimental characterization of failures can become unfeasible for several reasons. Thus, computer simulation of faulty motors digital twins can be an important alternative for failure analysis, especially in large motors. From this perspective, this paper presents and discusses several limitations found in the technical literature that can be minimized with the implementation of digital twins. In addition, a 3D finite element model of an induction motor with broken rotor bars is demonstrated, and motor current signature analysis is used to verify the fault effects. Results are analyzed in the time and frequency domain. Additionally, an artificial neural network of the multilayer perceptron type is used to classify the failure of broken bars in the 3D model rotor. |
| Databáze: | OpenAIRE |
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