scholarly journals Cloud based bearing fault diagnosis of induction motors

2021 ◽  
Author(s):  
Aydil BAPİR ◽  
İlhan AYDIN
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1884 ◽  
Author(s):  
Rafia Nishat Toma ◽  
Alexander E. Prosvirin ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Statistical Features ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Motor Current ◽  
Comparison Results

Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults.

Bearing Fault Diagnostic of Induction Motor by Stator Currents Analysis Based on Park’s Vectors Approach

2014 ◽  
Vol 698 ◽  
pp. 83-89 ◽  
Author(s):  
Nail Safin ◽  
Vladimir Prakht ◽  
Vladimir Dmitrievskii
Keyword(s):  
Fault Diagnosis ◽  
Early Detection ◽  
Induction Motor ◽  
Induction Motors ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Stator Current ◽  
Bearing Defects ◽  
Bearing Damage ◽  
Vector Approach

This paper deals with the possibilities of bearing fault diagnosis of induction motors by stator currents analysis based on Park’s vector approach. The main theoretical aspects, the experimental results and their analysis are presented. It is shown that preventive stator current diagnostics of induction motors based on Park’s vector allows us early detection of bearing defects in the inner and outer races. The current spectra additional harmonics appearing in the case of bearing damage are found.

scholarly journals Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals

2015 ◽  
Vol 10 (4) ◽  
pp. 1558-1565 ◽  
Author(s):  
Don-Ha Hwang ◽  
Young-Woo Youn ◽  
Jong-Ho Sun ◽  
Kyeong-Ho Choi ◽  
Jong-Ho Lee ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis
Sign in / Sign up

Export Citation Format

Share Document