scholarly journals An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

Energies ◽  
2018 ◽  
Vol 11 (3) ◽  
pp. 653 ◽  
Author(s):  
Luqman Maraaba ◽  
Zakariya Al-Hamouz ◽  
Mohammad Abido
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Diagnosis Tool

Diagnostic Fault Simulation for the Failure Analyst

2004 ◽  
Author(s):  
Dan Bodoh ◽  
Anthony Blakely ◽  
Terry Garyet
Keyword(s):  
Fault Diagnosis ◽  
Failure Analysis ◽  
Complete Solution ◽  
Fault Simulation ◽  
Building Blocks ◽  
Physical World ◽  
Design For Test ◽  
Fault Diagnosis System ◽  
Diagnosis Algorithm ◽  
Diagnosis Tool

Abstract Since failure analysis (FA) tools originated in the design-for-test (DFT) realm, most have abstractions that reflect a designer's viewpoint. These abstractions prevent easy application of diagnosis results in the physical world of the FA lab. This article presents a fault diagnosis system, DFS/FA, which bridges the DFT and FA worlds. First, it describes the motivation for building DFS/FA and how it is an improvement over off-the-shelf tools and explains the DFS/FA building blocks on which the diagnosis tool depends. The article then discusses the diagnosis algorithm in detail and provides an overview of some of the supporting tools that make DFS/FA a complete solution for FA. It also presents a FA example where DFS/FA has been applied. The example demonstrates how the consideration of physical proximity improves the accuracy without sacrificing precision.

scholarly journals Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors

2020 ◽  
Vol 11 (1) ◽  
pp. 314
Author(s):  
Gustavo Henrique Bazan ◽  
Alessandro Goedtel ◽  
Marcelo Favoretto Castoldi ◽  
Wagner Fontes Godoy ◽  
Oscar Duque-Perez ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Mutual Information ◽  
Induction Motors ◽  
Operating Conditions ◽  
Industrial Systems ◽  
Bearing Fault Diagnosis ◽  
Bee Colony ◽  
Three Phase ◽  
C4.5 Decision Tree ◽  
The Time Domain

Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.

scholarly journals Interturn Fault Diagnosis in Induction Motors Using the Pendulous Oscillation Phenomenon

2006 ◽  
Vol 21 (4) ◽  
pp. 871-882 ◽  
Author(s):  
Behrooz Mirafzal ◽  
Richard J. Povinelli ◽  
Nabeel A. O. Demerdash
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Oscillation Phenomenon

Hybrid algorithmic approach oriented to incipient rotor fault diagnosis on induction motors

2018 ◽  
Vol 80 ◽  
pp. 427-438 ◽  
Author(s):  
Ignacio Martin-Diaz ◽  
Daniel Morinigo-Sotelo ◽  
Oscar Duque-Perez ◽  
Roque A. Osornio-Rios ◽  
Rene J. Romero-Troncoso
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Algorithmic Approach
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