Analytic Model for Induction Motors Under Localized Bearing Faults

2018 ◽  
Vol 33 (2) ◽  
pp. 617-626 ◽  
Author(s):  
Mansour Ojaghi ◽  
Mahdi Sabouri ◽  
Jawad Faiz
Keyword(s):  
Induction Motors ◽  
Analytic Model ◽  
Bearing Faults

scholarly journals A New Hybrid Diagnosis of Bearing Faults Based on Time-Frequency Images and Sparse Representation

2020 ◽  
Vol 37 (6) ◽  
pp. 907-918
Author(s):  
Ilhan Aydin ◽  
Seyfullah Kaner
Keyword(s):  
Deep Learning ◽  
Induction Motors ◽  
Early Stage ◽  
Training Data ◽  
Bearing Faults ◽  
Time Frequency ◽  
Histograms Of Oriented Gradients ◽  
Diagnosis Method ◽  
And Performance ◽  
Learning Machine

Induction motors are an essential component of many applications in industry due to their robust and simple construction. Since bearing faults are the most occurred fault type in the induction motors, it is important to implement the fault detection procedure at an early stage to prevent a sudden interruption of industrial systems. In recent years, deep learning-based techniques have become important tools for converting raw data into images and for producing high-quality images. However, deep learning-based techniques are still difficult to apply in real-time because the techniques require large training data, which slows down the learning process. In the present study, we propose a novel bearing faults diagnosis method at different operating speeds and load conditions. We obtain the time-frequency (TF) representation by applying continuous wavelet analysis to the raw vibration signals. The results of TF representation is recorded as an image. We apply co-occurrence Histograms of Oriented Gradients (coHOG) to the image to obtain features and classify the features with extreme learning machine with a sparse classifier (ELMSRC) to diagnose faults. We obtained better results in terms of time and performance compared with the proposed method of other classification and deep learning techniques.

scholarly journals Effects of the Slot Harmonics on the Stator Current in an Induction Motor with Bearing Fault

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangjin Song ◽  
Jingtao Hu ◽  
Hongyu Zhu ◽  
Jilong Zhang
Keyword(s):  
Induction Motor ◽  
Analytical Approach ◽  
Induction Motors ◽  
Experimental Tests ◽  
Fault Models ◽  
Popular Method ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Stator Current ◽  
Theoretical Inference

Bearing faults are the most frequent faults of induction motors. The current spectrum analysis is an easy and popular method for the monitoring and detection of bearing faults. After a presentation of the existing fault models, this paper illustrates an analytical approach to evaluate the effects of the slot harmonics on the stator current in an induction motor with bearing fault. Simple and clear theoretical analysis results in some new current characteristic frequencies. Experimental tests with artificial bearing outer raceway fault are carried out by measuring stator current signals. The experimental results by spectral analysis of the stator current agree well with the theoretical inference.

Diagnosis of Bearing Faults of Induction Motors by Spectral Analysis of Stator Currents

2014 ◽  
Vol 1070-1072 ◽  
pp. 1187-1190 ◽  
Author(s):  
Nail R. Safin ◽  
Vladimir A. Prakht ◽  
Vladimir A. Dmitrievskii ◽  
Anton A. Dmitrievskii
Keyword(s):  
Spectral Analysis ◽  
Induction Motor ◽  
Induction Motors ◽  
Air Gap ◽  
Bearing Faults ◽  
Proper Diagnosis

The article is dedicated to investigate possibility of diagnostics bearing faults of induction motors by stator currents analysis. The main features of stator currents analysis are studied. The possibility of identifying air-gap eccentricity due to the working with damaged bearings by stator currents investigating is revealed. The recommendations of monitoring and configuration of a proper diagnosis of induction motor are provided.

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.

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