Vibrations Measurement and Current Signatures for Fault Detection in Asynchronous Motor

2020 ◽  
Author(s):  
M. Avoci Ugwiri ◽  
M. Carratu ◽  
A. Pietrosanto ◽  
V. Paciello ◽  
A. Lay-Ekuakille
Keyword(s):  
Fault Detection ◽  
Asynchronous Motor ◽  
Current Signatures

Detection and Diagnosis of Broken Rotor Bars in Induction Motors Using the Fuzzy Min-Max Neural Network

2012 ◽  
Vol 3 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Manjeevan Seera ◽  
Chee Peng Lim ◽  
Dahaman Ishak
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Induction Motors ◽  
Fault Detection And Diagnosis ◽  
Broken Rotor Bars ◽  
Power Spectral ◽  
Motor Current Signature Analysis ◽  
Detection And Diagnosis ◽  
Current Signatures

In this paper, a fault detection and diagnosis system for induction motors using motor current signature analysis and the Fuzzy Min-Max (FMM) neural network is described. The finite element method is first employed to generate experimental data for predicting the changes in stator current signatures of an induction motor due to broken rotor bars. Then, a series real laboratory experiments is for broken rotor bars detection and diagnosis. The induction motor with broken rotor bars is operated under different load conditions. In all the experiments, the FMM network is used to learn and distinguish between normal and faulty states of the induction motor based on the input features extracted from the power spectral density. The experimental results positively demonstrate that the FMM network is useful for fault detection and diagnosis of broken rotor bars in induction motors.

scholarly journals Multiclass Incremental Learning for Fault Diagnosis in Induction Motors Using Fine-Tuning with a Memory of Exemplars and Nearest Centroid Classifier

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Magdiel Jiménez-Guarneros ◽  
Jonas Grande-Barreto ◽  
Jose de Jesus Rangel-Magdaleno
Keyword(s):  
Fault Detection ◽  
Incremental Learning ◽  
Induction Motors ◽  
Asynchronous Motor ◽  
Feature Space ◽  
Fine Tuning ◽  
Electromechanical Systems ◽  
Critical Data ◽  
Public Datasets ◽  
Centroid Classifier

Early detection of fault events through electromechanical systems operation is one of the most attractive and critical data challenges in modern industry. Although these electromechanical systems tend to experiment with typical faults, a common event is that unexpected and unknown faults can be presented during operation. However, current models for automatic detection can learn new faults at the cost of forgetting concepts previously learned. This article presents a multiclass incremental learning (MCIL) framework based on 1D convolutional neural network (CNN) for fault detection in induction motors. The presented framework tackles the forgetting problem by storing a representative exemplar set from past data (known faults) in memory. Then, the 1D CNN is fine-tuned over the selected exemplar set and data from new faults. Test samples are classified using nearest centroid classifier (NCC) in the feature space from 1D CNN. The proposed framework was evaluated and validated over two public datasets for fault detection in induction motors (IMs): asynchronous motor common fault (AMCF) and Case Western Reserve University (CWRU). Experimental results reveal the proposed framework as an effective solution to incorporate and detect new induction motor faults to already known, with a high accuracy performance across different incremental phases.

Asynchronous Motor Fault Detection Circuit Design

2014 ◽  
Author(s):  
Zongming Li ◽  
Baichao An ◽  
Haoyu Lu ◽  
Qiang Liu ◽  
Liying Yuan
Keyword(s):  
Fault Detection ◽  
Circuit Design ◽  
Asynchronous Motor ◽  
Detection Circuit

scholarly journals Fault Detection (Condition Monitoring) of Induction Motor based on Wavelet Transform

2013 ◽  
pp. 268-271
Author(s):  
U. E. Hiwase ◽  
S. B. Warkad
Keyword(s):  
Wavelet Transform ◽  
Steady State ◽  
Fault Detection ◽  
Induction Motor ◽  
Condition Monitoring ◽  
Discrete Wavelet ◽  
Steady State Analysis ◽  
Motor Current ◽  
Current Signatures ◽  
Vector Approach

Presently, many condition monitoring techniques that are based on steady-state analysis are being applied to Induction motor. However, the operation of induction motor is predominantly transient, therefore prompting the development of non-stationary techniques for fault detection. In this paper we apply steady-state techniques e.g. Motor Current Signatures Analysis (MCSA) and the Extended Park’s Vector Approach (EPVA), as well as a new transient technique that is a combination of the EPVA, the Discrete Wavelet Transform and statistics, to the detection of turn faults in a induction motor. It will be shown that steady-state techniques are not effective when applied to induction motor operating under transient.

Planetary Gear Fault Detection Based on Mechanical Torque and Stator Current Signatures of a Wound Rotor Induction Generator

2018 ◽  
Vol 33 (3) ◽  
pp. 1072-1085 ◽  
Author(s):  
Mohammad Hoseintabar Marzebali ◽  
Jawad Faiz ◽  
Gerard-Andre Capolino ◽  
Shahin Hedayati Kia ◽  
Humberto Henao
Keyword(s):  
Fault Detection ◽  
Planetary Gear ◽  
Induction Generator ◽  
Stator Current ◽  
Gear Fault ◽  
Current Signatures

Asynchronous Motor Bearing Fault Detection Methods

2011 ◽  
Vol 383-390 ◽  
pp. 5055-5058
Author(s):  
Li Ling Sun ◽  
Bao Long Zhang
Keyword(s):  
Fault Detection ◽  
Failure Probability ◽  
Asynchronous Motor ◽  
Detection Methods ◽  
Ball Bearings ◽  
Bearing Failure ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Deep Groove ◽  
Motor Bearing

Bearing, asynchronous deep groove ball bearings are widely used in induction motor field. Motor bearing failure probability is as high as 40% in asynchronous motor. It accounts for the largest proportion of failures in the motor. Therefore, people have been on studying motor bearing fault detection methods for further research. So far, people have studied a variety of modern detection methods.

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