scholarly journals Analytical Model for Separated Frequency Signature of Outer Race Bearing Fault From Static Eccentricity

2022 ◽  
Vol 15 ◽  
pp. 1821-1827
Author(s):  
Touil Abderrahim ◽  
Babaa Fatima ◽  
Bennis Ouafae ◽  
Kratz Frederic
Keyword(s):  
Mathematical Model ◽  
Analytical Model ◽  
Induction Motors ◽  
Function Theory ◽  
Outer Race ◽  
Bearing Fault ◽  
Squirrel Cage ◽  
Three Phase ◽  
Analytical Development ◽  
Static Eccentricity

The present paper addresses a precise and an accurate mathematical model for three-phase squirrel cage induction motors, based on winding function theory. Through an analytical development, a comparative way is presented to separate the signature between the existence of the outer race bearing fault and the static eccentricity concerning the asymmetry of the air gap between the stator and the rotor. This analytical model proposes an effective signature of outer race defect separately from other signatures of static eccentricity. Simulation and experimental results are presented to validate the proposed analytical model.

scholarly journals Sensors Applied to Bearing Fault Detection in Three-Phase Induction Motors

2021 ◽  
Vol 10 (1) ◽  
pp. 40
Author(s):  
Guilherme Beraldi Lucas ◽  
Bruno Albuquerque de Castro ◽  
Paulo José Amaral Serni ◽  
Rudolf Ribeiro Riehl ◽  
André Luiz Andreoli
Keyword(s):  
Fault Detection ◽  
Induction Motors ◽  
Heat Emission ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Vibration Sensors ◽  
Three Phase ◽  
Infra Red ◽  
Rogowski Coils

Three-Phase Induction Motors (TIMs) are widely applied in industries. Therefore, there is a need to reduce operational and maintenance costs since their stoppages can impair production lines and lead to financial losses. Among all the TIM components, bearings are crucial in the machine operation once they couple rotor to the motor frame. Furthermore, they are constantly subjected to friction and mechanical wearing. Consequently, they represent around 41% of the motor fault, according to IEEE. In this context, several studies have sought to develop monitoring systems based on different types of sensors. Therefore, considering the high demand, this article aims to present the state of the art of the past five years concerning the sensing techniques based on current, vibration, and infra-red analysis, which are characterized as promising tools to perform bearing fault detection. The current and vibration analysis are powerful tools to assess damages in the inner race, outer race, cages, and rolling elements of the bearings. These sensing techniques use current sensors like hall effect-based, Rogowski coils, and current transformers, or vibration sensors such as accelerometers. The effectiveness of these techniques is due to the previously developed models, which relate the current and vibration frequencies to the origin of the fault. Therefore, this article also presents the bearing fault mathematical modeling for these techniques. The infra-red technique is based on heat emission, and several image processing techniques were developed to optimize bearing fault detection, which is presented in this review. Finally, this work is a contribution to pushing the frontiers of the bearing fault diagnosis area.

Facilitation for Load Based Energy Savings in Three–phase Squirrel Cage Induction Motors

2006 ◽  
Vol 103 (6) ◽  
pp. 37-64
Author(s):  
B. Gopalakrishnan ◽  
S. Chaudhari ◽  
P. Famouri ◽  
R. W. Plummer
Keyword(s):  
Energy Savings ◽  
Induction Motors ◽  
Squirrel Cage ◽  
Three Phase

scholarly journals Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 8033
Author(s):  
Juan-Jose Saucedo-Dorantes ◽  
Israel Zamudio-Ramirez ◽  
Jonathan Cureno-Osornio ◽  
Roque Alfredo Osornio-Rios ◽  
Jose Alfonso Antonino-Daviu
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
High Performance ◽  
Induction Motors ◽  
Statistical Features ◽  
Monitoring Method ◽  
Data Set ◽  
Linear Discriminant ◽  
Outer Race ◽  
Bearing Fault

Bearings are the elements that allow the rotatory movement in induction motors, and the fault occurrence in these elements is due to excessive working conditions. In induction motors, electrical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that gradually increase to irreparable damages. Thus, condition monitoring strategies capable of assessing bearing fault severities are mandatory to overcome this critical issue. The contribution of this work lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of different fault severities of the outer race bearing fault in an induction motor. The proposed approach is supported by fusion information of different physical magnitudes and the use of Machine Learning and Artificial Intelligence. An important aspect of this proposal is the calculation of a hybrid-set of statistical features that are obtained to characterize vibration and stator current signals by its processing through domain analysis, i.e., time-domain and frequency-domain; also, the fusion of information of both signals by means of the Linear Discriminant Analysis is important due to the most discriminative and meaningful information is retained resulting in a high-performance condition characterization. Besides, a Neural Network-based classifier allows validating the effectiveness of fusion information from different physical magnitudes to face the diagnosis of multiple fault severities that appear in the bearing outer race. The method is validated under an experimental data set that includes information related to a healthy condition and five different severities that appear in the outer race of bearings.

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