Bearing Fault Prediction Using Current Signature Analysis in Electric Water Pump

Author(s):  
M. Yuvaraj ◽  
P. Elzhiloan ◽  
T. Thiruvaran ◽  
V. Aravinthan ◽  
B. Thanatheepan
Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.


Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4514
Author(s):  
Vincent Becker ◽  
Thilo Schwamm ◽  
Sven Urschel ◽  
Jose Alfonso Antonino-Daviu

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.


2021 ◽  
Vol 5 (1) ◽  
pp. PRESS
Author(s):  
Ramadoni Syahputra ◽  
Hedi Purwanto ◽  
Rama Okta Wiyagi ◽  
Muhamad Yusvin Mustar ◽  
Indah Soesanti

This paper discusses the analysis of the performance of an induction motor using the motor current signature analysis (MCSA) technique. Induction motor is a type of electric machine that is widely used in industry. One of the industries that utilize induction motors is a steam power plant (SPP). The role of induction motors is very vital in SPP operations. Therefore, it is necessary to monitor the performance, stability, and efficiency to anticipate disturbances that can cause damage or decrease the life of the induction motor. MCSA is a reliable technique that can be used to analyze damage to an induction motor. In this technique, the induction motor current signal is detected using a current transducer. The signal is then passed on to the signal conditioning and then into the data acquisition device. The important signal data is analyzed in adequate computer equipment. The results of this analysis determine the condition of the induction motor, whether it is normal or damaged. In this research, a case study was carried out at the Rembang steam power plant, Central Java, Indonesia. The results of the analysis of several induction motors show that most of them are in normal conditions and are still feasible to operate.


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