Application of Wavelet Analysis in Condition Monitoring of Induction Motors

2020 ◽  
pp. 795-807
Author(s):  
Amandeep Sharma ◽  
Pankaj Verma ◽  
Anurag Choudhary ◽  
Lini Mathew ◽  
Shantanu Chatterji
Keyword(s):  
Wavelet Analysis ◽  
Condition Monitoring ◽  
Induction Motors

scholarly journals Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 304
Author(s):  
Sakthivel Ganesan ◽  
Prince Winston David ◽  
Praveen Kumar Balachandran ◽  
Devakirubakaran Samithas
Keyword(s):  
Standard Deviation ◽  
Power Quality ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Classification Accuracy ◽  
Induction Motors ◽  
Quality Data ◽  
Starting Current ◽  
Quality Issues ◽  
Condition Monitoring System

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

Advanced Diagnosis of Outer Cage Damage in Double-Squirrel-Cage Induction Motors Under Time-Varying Conditions Based on Wavelet Analysis

2014 ◽  
Vol 50 (3) ◽  
pp. 1791-1800 ◽  
Author(s):  
Yasser Gritli ◽  
Sang Bin Lee ◽  
Fiorenzo Filippetti ◽  
Luca Zarri
Keyword(s):  
Wavelet Analysis ◽  
Induction Motors ◽  
Time Varying ◽  
Squirrel Cage

Bond Graph Modelling for Condition Monitoring of Induction Motors

2020 ◽  
pp. 511-523
Author(s):  
Aisha Alashter ◽  
Yunpeng Cao ◽  
Khalid Rabeyee ◽  
Samir Alabied ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Induction Motors ◽  
Bond Graph

scholarly journals Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3279 ◽  
Author(s):  
Zuolu Wang ◽  
Jie Yang ◽  
Haiyang Li ◽  
Dong Zhen ◽  
Yuandong Xu ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Condition Monitoring ◽  
Induction Motors ◽  
High Reliability ◽  
Industrial Applications ◽  
Experimental Investigations ◽  
Signature Analysis ◽  
Step Size ◽  
Time Service ◽  
Vibration Signature

Induction motors (IMs) play an essential role in the field of various industrial applications. Long-time service and tough working situations make IMs become prone to a broken rotor bar (BRB) that is one of the major causes of IMs faults. Hence, the continuous condition monitoring of BRB faults demands a computationally efficient and accurate signal diagnosis technique. The advantage of high reliability and wide applicability in condition monitoring and fault diagnosis based on vibration signature analysis results in an improved cyclic modulation spectrum (CMS), which is one of the cyclic spectral analysis algorithms. CMS is proposed in this paper for the detection and identification of BRB faults in IMs at a steady-state operation based on a vibration signature analysis. The application of CMS is based on the short-time Fourier transform (STFT) and the improved CMS approach is attributed to the optimization of STFT. The optimal window is selected to improve the accuracy for identifying the BRB fault types and severities. The appropriate window length and step size are optimized based on the selected window function to receive a better calculation benefit through simulation and experimental analysis. Compared to other estimators, the improved CMS method provides better fault detectability results by analyzing vertical vibration signatures of a healthy motor, and damaged motors with 1 BRB and 2 BRBs under 0%, 20%, 40%, 60%, and 80% load conditions. Both synthetic and experimental investigations demonstrate the proposed methodology can significantly reduce computational costs and identify the BRB fault types and severities effectively.

Fault diagnostic of induction motors for equipment reliability and health maintenance based upon Fourier and wavelet analysis

2005 ◽  
Vol 9 (3) ◽  
pp. 112-116 ◽  
Author(s):  
Hyeon Bae ◽  
Youn-Tae Kim ◽  
Sang-Hyuk Lee ◽  
Sungshin Kim ◽  
Man Hyung Lee
Keyword(s):  
Wavelet Analysis ◽  
Induction Motors ◽  
Health Maintenance ◽  
Equipment Reliability ◽  
Fault Diagnostic
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