Journal ArticleDOI
Fault Diagnosis of Bearing Damage by Means of the Linear Discriminant Analysis of Stator Current Features From the Frequency Selection
TLDR
In this paper, the authors proposed a diagnostic system based on the current feature generated by a frequency selection in the stator current spectrum, which is evaluated by means of linear discriminant analysis and the fault diagnosis is performed with the Bayes classifier.Abstract:
Bearing damage is the most common failure in electrical machines. It can be detected by vibration analysis. However, this diagnosis method is costly or not always accessible due to the location of the equipment and the choice of the implemented sensors. An alternative method is provided with the electrical monitoring using the stator current of the electrical machine. This study aims at developing a diagnostic system based on the current feature generated by a frequency selection in the stator current spectrum. The features are evaluated by means of the linear discriminant analysis and the fault diagnosis is performed with the Bayes classifier. The proposed method is evaluated by two types of damages at different load cases. The results show that the damaged bearings can be distinguished from the healthy bearing depending on the considered load cases.read more
Citations
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Journal ArticleDOI
Diagnosis of the three-phase induction motor using thermal imaging
Adam Glowacz,Z. Głowacz +1 more
TL;DR: The authors develop an original method of the feature extraction of thermal images MoASoID (Method of Areas Selection of Image Differences), which compares many training sets together and it selects the areas with the biggest changes for the recognition process.
Journal ArticleDOI
A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion
Duy Tang Hoang,Hee-Jun Kang +1 more
TL;DR: A motor CS-based fault diagnosis method utilizing deep learning and information fusion (IF), which can be applied to external bearings in rotary machine systems and is verified through experiments carried out with actual bearing fault signals.
Journal ArticleDOI
A new bearing fault diagnosis method based on modified convolutional neural networks
TL;DR: An intelligent diagnosis algorithm based on Convolution Neural Network is proposed, which can automatically accomplish the process of the feature extraction and fault diagnosis and can meet the timeliness requirements of fault diagnosis.
Journal ArticleDOI
Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers.
TL;DR: 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 and demonstrates that the suggested technique is promising for diagnosis of IM bearing faults.
Journal ArticleDOI
A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning
TL;DR: This paper first reviews the fundamentals of prognostics and health management techniques for REBs, and provides overviews of contemporary REB PHM techniques with a specific focus on modern artificial intelligence (AI) techniques (i.e., shallow learning algorithms).
References
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Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring
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Journal ArticleDOI
Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis
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Proceedings ArticleDOI
An unsupervised, on-line system for induction motor fault detection using stator current monitoring
TL;DR: In this article, a new method for online induction motor fault detection is presented, which utilizes artificial neural networks to learn the spectral characteristics of a good motor operating online, which may contain many harmonics due to the load which correspond to normal operating conditions.