Inner and Outer Race Bearing Defects of Induction Motor Running at Low Speeds Signal Analysis with DWT

2021 ◽  
pp. 975-983
Author(s):  
Bellal Belkacemi ◽  
Salah Saad
Keyword(s):  
Induction Motor ◽  
Signal Analysis ◽  
Outer Race ◽  
Bearing Defects ◽  
Low Speeds

Quaternion Signal Analysis Algorithm for Induction Motor Fault Detection

2019 ◽  
Vol 66 (11) ◽  
pp. 8843-8850 ◽  
Author(s):  
Jose L. Contreras-Hernandez ◽  
Dora Luz Almanza-Ojeda ◽  
Sergio Ledesma-Orozco ◽  
Arturo Garcia-Perez ◽  
Rene J. Romero-Troncoso ◽  
...  
Keyword(s):  
Fault Detection ◽  
Induction Motor ◽  
Signal Analysis ◽  
Analysis Algorithm ◽  
Quaternion Signal

Concordia Transform-Based Current Analysis for Induction Motor Diagnosis

2013 ◽  
Vol 569-570 ◽  
pp. 481-488
Author(s):  
Jin Jiang Wang ◽  
Robert X. Gao ◽  
Ru Qiang Yan
Keyword(s):  
Induction Motor ◽  
Instantaneous Frequency ◽  
Low Cost ◽  
Experimental Studies ◽  
Current Analysis ◽  
New Approach ◽  
Non Invasive ◽  
Bearing Defects ◽  
Bearing Defect ◽  
Three Phase

This paper presents a new approach for bearing defect diagnosis in induction motor by taking advantage of three-phase stator current analysis based on Concordia transform. The current signature caused by bearing defect is firstly analyzed using an analytic model. Concordia transform is performed to extract the instantaneous frequency based on phase demodulation. The bearing defect feature is then identified via spectrum analysis of the variation of current instantaneous frequency. Both simulation and experimental studies are performed to demonstrate the effectiveness of proposed method in identifying bearing defects. The method is inherently low cost, non-invasive, and computational efficient, making it a good candidate for various applications.

scholarly journals Condition Monitoring of Three Phase Induction Motor using Current Signature Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 1946-1955
Author(s):  
Sai Sharath Podduturi
Keyword(s):  
Induction Motor ◽  
Condition Monitoring ◽  
Vibration Monitoring ◽  
Signature Analysis ◽  
Monitoring Methods ◽  
Bearing Faults ◽  
Outer Race ◽  
Gabor Analysis ◽  
Current Monitoring ◽  
Current Signature

In this paper we are going to see how Gabor transform is used to analyze the signal and to determine the inner and outer race of bearing faults by monitoring the condition of Induction motor using Motor Current Signature Analysis. Among the various faults bearing faults is the major problem, which cause a huge damage to induction motor, when unnoticed at developing stage. So, monitoring of bearing faults is very important and it can done by several conditions monitoring methods like thermal monitoring, vibration monitoring and more but these methods require expensive sensors or specified tools, whereas current monitoring methods doesn’t require any additional tools. Usually, this condition monitoring is used to detect the various faults like bearing faults, load faults by MCSA. If the fault is present in the motor, the frequency spectrum of the line current is different from healthy ones, the Gabor analysis detects the fault signature generated in the induction motor, by using mathematical expressions and calculate the RMS and Standard deviation values, these fault values are different from healthy ones. Through this we can identify faults.

An Experimental Approach in Defect Detection of a Single Row Ball Bearing Using Noise Generation Signal

2019 ◽  
Author(s):  
Aref Afsharfard ◽  
Seyed Hamid Reza Sanei
Keyword(s):  
Ball Bearing ◽  
Experimental Testing ◽  
Local Defect ◽  
Noise Generation ◽  
Frequency Spectra ◽  
Single Row ◽  
Outer Race ◽  
Bearing Defects ◽  
Cost Efficient ◽  
Local Defects

Abstract Bearings are critical mechanical components that are used in rotary machinery. Timely detection of defects in such components can prevent catastrophic failure. Noise is generated during the rotation of bearings even without the presence of defects due to finite number of rotating elements to carry the load. Such noise is associated with the change in effective stiffness during rotation, however, a sharp spike is observed in the noise level with presence of local defects. This study uses the noise generation aspect of roller bearings to identify local defect in a single row ball bearing with outer race stationary under radial load. Experimental testing is conducted on two identical bearings. The defective bearing is selected from a diesel engine subjected to 20 years of service. Dissecting the defective bearing revealed pitting and spalling of the inner race and balls, the most two common bearing defects. Both time and frequency analysis of sound pressure generated by the bearings were performed. The results show that there is a clear distinction in the time and frequency spectra between healthy and defective bearings. Findings of this study revealed that using a simple cost efficient in-house experimental setup, local defects can be readily detected.

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