Electrical Submersible Pumps Condition Monitoring Using Motor Current Signature Analysis

2018 ◽  
Author(s):  
Peter Popaleny ◽  
Ahmet Duyar ◽  
Cumali Ozel ◽  
Yusuf Erdogan
Keyword(s):  
Condition Monitoring ◽  
Signature Analysis ◽  
Motor Current ◽  
Electrical Submersible Pumps ◽  
Motor Current Signature Analysis ◽  
Current Signature

scholarly journals Comparison of Vibration, Sound and Motor Current Signature Analysis for Detection of Gear Box Faults

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
T Praveenkumar ◽  
M Saimurugan ◽  
K I Ramachandran
Keyword(s):  
Condition Monitoring ◽  
Signal Analysis ◽  
Maintenance Cost ◽  
Support Vector ◽  
Signature Analysis ◽  
Motor Current ◽  
Motor Current Signature Analysis ◽  
Hybrid Electric ◽  
Gear Box ◽  
Current Signature

Gear box is used in automobiles and industries for power transmission under different working conditions and applications. Failure in a gear box at unexpected time leads to increase in machine downtime and maintenance cost. In order to overcome these losses, the most effective condition monitoring technique has to be used for early detection of faults. Vibration and sound signal analysis have been used for monitoring the condition of rotating machineries. Motor Current Signature Analysis (MCSA) has rarely been used in gearbox condition monitoring. This work presents a methodology based on vibration, sound and motor current signal analysis for diagnosis of gearbox faults under various simulated gear and bearing fault conditions. Statistical features were extracted from the raw data of these three transducer signals and the best features were selected from the extracted features. Then the selected features were given as an input to Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers and their performances were compared. In recent years, Hybrid Electric Vehicles (HEV) are gaining more interest for their advances and this work had a scope in monitoring the power loss in hybrid electric vehicle gearbox using MCSA.

scholarly journals Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4514
Author(s):  
Vincent Becker ◽  
Thilo Schwamm ◽  
Sven Urschel ◽  
Jose Alfonso Antonino-Daviu
Keyword(s):  
Condition Monitoring ◽  
False Alarms ◽  
Signature Analysis ◽  
Variable Speed ◽  
Monitoring Methods ◽  
Time Frequency ◽  
Motor Current ◽  
Load Point ◽  
Bearing Defects ◽  
Current Signature

The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only a few contributions to current-based pump diagnosis. In this paper, two current-based methods for the detection of bearing defects, impeller clogging, and cracked impellers are presented. The first approach, load point-dependent fault indicator analysis (LoPoFIA), is an approach that was derived from motor current signature analysis (MCSA). Compared to MCSA, the novelty of LoPoFIA is that only amplitudes at typical fault frequencies in the current spectrum are considered as a function of the hydraulic load point. The second approach is advanced transient current signature analysis (ATCSA), which represents a time-frequency analysis of a current signal during start-up. According to the literature, ATCSA is mainly used for motor diagnosis. As a test item, a VSD-driven circulation pump was measured in a pump test bench. Compared to MCSA, both LoPoFIA and ATCSA showed improvements in terms of minimizing false alarms. However, LoPoFIA simplifies the separation of bearing defects and impeller defects, as impeller defects especially influence higher flow ranges. Compared to LoPoFIA, ATCSA represents a more efficient method in terms of minimizing measurement effort. In summary, both LoPoFIA and ATCSA provide important insights into the behavior of faulty pumps and can be advantageous compared to MCSA in terms of false alarms and fault separation.

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