scholarly journals Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3392 ◽  
Author(s):  
Oscar Duque-Perez ◽  
Carlos Del Pozo-Gallego ◽  
Daniel Morinigo-Sotelo ◽  
Wagner Fontes Godoy
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Induction Motors ◽  
Bearing Faults ◽  
Stator Current ◽  
Shrinkage Methods ◽  
Open Issue ◽  
Bearing Monitoring ◽  
Types Of Faults

Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults, the use of vibrations or sound generally offers better results in the accuracy of the detection, although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has many more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal.

scholarly journals Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods

2019 ◽  
Author(s):  
Oscar Duque-Perez ◽  
Carlos Del Pozo-Gallego ◽  
Daniel Morinigo-Sotelo ◽  
Wagner Fontes Godoy
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Induction Motors ◽  
Bearing Faults ◽  
Stator Current ◽  
Shrinkage Methods ◽  
Open Issue ◽  
Bearing Monitoring ◽  
Types Of Faults

Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults the use of vibrations or sound generally offers better results in the accuracy of the detection although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has much more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal

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.

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.

scholarly journals Broken rotor bars fault detection in induction motors based on current envelope and neural network

2021 ◽  
Vol 3 (2 (111)) ◽  
pp. 88-95
Author(s):  
Mohammed Obaid Mustafa
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
Induction Motors ◽  
Fault Classification ◽  
Monitoring Method ◽  
Broken Rotor Bars ◽  
Stator Current ◽  
Motor Current ◽  
Machine Failure ◽  
Feature Extraction And Selection

The growing demand for dependable manufacturing techniques has sped up research into condition monitoring and fault diagnosis of critical motor parts. On the other hand, in modern industry, machine maintenance is becoming increasingly necessary. An insufficient maintenance strategy can result in unnecessarily high downtime or accidental machine failure, resulting in significant financial and even human life losses. Downtime and repair costs rise as a result of failure. Furthermore, developing an online condition monitoring method may be one solution to come up for the problem. Early detection of faults is very vital since they grow quickly and can cause further problems to the motor. This paper proposes an effective strategy for the classification of broken rotor bars (BRBs) for induction motors (IMs) that uses a new approach based on Artificial Neural Network (ANN) and stator current envelope. The stator current envelope is extracted using the cubic spline interpolation process. This is based on the idea that the amplitude-modulated motor current signal can be revealed using the motor current envelope. The stator current envelope is used to select seven features, which will be used as input for the neural network. Five IM conditions were experimentally used in this study, including a part of BRB, 1 BRB, 2 BRBs and 3 BRBs. The new feature extraction and selection approach achieves a higher level of accuracy than the conventional method for motor fault classification, according to the experimental results. Indeed, the results are impressive, and it is capable of detecting the exact number of broken rotor bars under full load conditions

A Structured Approach to Machine Learning for Condition Monitoring: A Case Study

2021 ◽  
pp. 55-76
Author(s):  
Jacopo Cavalaglio Camargo Molano ◽  
Federico Campo ◽  
Luca Capelli ◽  
Giulia Massaccesi ◽  
Davide Borghi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Structured Approach
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