Diagnosis of roller bearing defects using neural networks

1993 ◽  
Vol 8 (4) ◽  
pp. 210-215 ◽  
Author(s):  
T. I. Liu ◽  
N. R. Iyer
Keyword(s):  
Neural Networks ◽  
Roller Bearing ◽  
Bearing Defects

Detection of Roller Bearing Conditions Using Mechatronics Approach

1999 ◽  
Author(s):  
T. I. Liu ◽  
F. Ordukhani
Keyword(s):  
Neural Networks ◽  
Roller Bearing ◽  
Diagnostic System ◽  
Feature Selection Technique ◽  
Vibration Signals ◽  
Selection Technique ◽  
On Line ◽  
Line Classification ◽  
Nonlinear Pattern

Abstract An on-line monitoring and diagnostic system is needed to detect faulty bearings. In this work, by applying the feature selection technique to the data obtained from vibration signals, six indices were selected. Artificial neural networks were used for nonlinear pattern recognition. An attempt was made to distinguish between normal and defective bearings. Counterpropagation neural networks with various network sizes were trained for these tasks. The counterpropagation neural networks were able to recognize a normal from a defective bearing with the success rate between 88.3% to 100%. The best results were obtained when all the six indices were used for the on-line classification of roller bearings.

scholarly journals Enhanced DET-Based Fault Signature Analysis for Reliable Diagnosis of Single and Multiple-Combined Bearing Defects

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
In-Kyu Jeong ◽  
Myeongsu Kang ◽  
Jaeyoung Kim ◽  
Jong-Myon Kim ◽  
Jeong-Min Ha ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Roller Bearing ◽  
Signature Analysis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Average Classification Accuracy ◽  
Bearing Defects ◽  
Bearing Failures ◽  
Cylindrical Roller Bearing

To early identify cylindrical roller bearing failures, this paper proposes a comprehensive bearing fault diagnosis method, which consists of spectral kurtosis analysis for finding the most informative subband signal well representing abnormal symptoms about the bearing failures, fault signature calculation using this subband signal, enhanced distance evaluation technique- (EDET-) based fault signature analysis that outputs the most discriminative fault features for accurate diagnosis, and identification of various single and multiple-combined cylindrical roller bearing defects using the simplified fuzzy adaptive resonance map (SFAM). The proposed comprehensive bearing fault diagnosis methodology is effective for accurate bearing fault diagnosis, yielding an average classification accuracy of 90.35%. In this paper, the proposed EDET specifically addresses shortcomings in the conventional distance evaluation technique (DET) by accurately estimating the sensitivity of each fault signature for each class. To verify the efficacy of the EDET-based fault signature analysis for accurate diagnosis, a diagnostic performance comparison is carried between the proposed EDET and the conventional DET in terms of average classification accuracy. In fact, the proposed EDET achieves up to 106.85% performance improvement over the conventional DET in average classification accuracy.

Fault Diagnosis of Rolling Bearing on the Basis of Wavelet Neural Network

2014 ◽  
Vol 598 ◽  
pp. 244-249
Author(s):  
Song Lin Wu ◽  
Jian Xin Liu ◽  
Li Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Bp Neural Network ◽  
Basis Function ◽  
Feature Vector ◽  
Roller Bearing ◽  
Rolling Bearing ◽  
Wavelet Neural Network

In this paper, the feature vector of the roller bearing signals are extracted on the basis of wavelet analysis and a fault diagnosis experiment is carried through wavelet neural network in detail. The method and the theory of fault diagnosis based on BP neural network and the radial basis function neural network are studied and the results of diagnosis based on relax-type Neural-Networks and close-type Neural-Networks are compared.

scholarly journals Bearing Defect Detection with Unsupervised Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jianqiao Xu ◽  
Zhaolu Zuo ◽  
Danchao Wu ◽  
Bing Li ◽  
Xiaoni Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Defect Detection ◽  
Surface Defects ◽  
Bearing Defects ◽  
Black Spots ◽  
Bearing Defect ◽  
Unsupervised Neural Network ◽  
Actual Use ◽  
Unsupervised Neural Networks

Bearings always suffer from surface defects, such as scratches, black spots, and pits. Those surface defects have great effects on the quality and service life of bearings. Therefore, the defect detection of the bearing has always been the focus of the bearing quality control. Deep learning has been successfully applied to the objection detection due to its excellent performance. However, it is difficult to realize automatic detection of bearing surface defects based on data-driven-based deep learning due to few samples data of bearing defects on the actual production line. Sample preprocessing algorithm based on normalized sample symmetry of bearing is adopted to greatly increase the number of samples. Two different convolutional neural networks, supervised networks and unsupervised networks, are tested separately for the bearing defect detection. The first experiment adopts the supervised networks, and ResNet neural networks are selected as the supervised networks in this experiment. The experiment result shows that the AUC of the model is 0.8567, which is low for the actual use. Also, the positive and negative samples should be labelled manually. To improve the AUC of the model and the flexibility of the samples labelling, a new unsupervised neural network based on autoencoder networks is proposed. Gradients of the unlabeled data are used as labels, and autoencoder networks are created with U-net to predict the output. In the second experiment, positive samples of the supervised experiment are used as the training set. The experiment of the unsupervised neural networks shows that the AUC of the model is 0.9721. In this experiment, the AUC is higher than the first experiment, but the positive samples must be selected. To overcome this shortage, the dataset of the third experiment is the same as the supervised experiment, where all the positive and negative samples are mixed together, which means that there is no need to label the samples. This experiment shows that the AUC of the model is 0.9623. Although the AUC is slightly lower than that of the second experiment, the AUC is high enough for actual use. The experiment results demonstrate the feasibility and superiority of the proposed unsupervised networks.

scholarly journals Roller Bearing Monitoring by New Subspace-Based Damage Indicator

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
G. Gautier ◽  
R. Serra ◽  
J.-M. Mencik
Keyword(s):  
Damage Identification ◽  
Roller Bearing ◽  
Simulated Data ◽  
Efficient Tool ◽  
Vibration Data ◽  
Damage Indicator ◽  
Bearing Defects ◽  
Damage Indicators ◽  
Bearing Characteristic ◽  
Bearing Monitoring

A frequency-band subspace-based damage identification method for fault diagnosis in roller bearings is presented. Subspace-based damage indicators are obtained by filtering the vibration data in the frequency range where damage is likely to occur, that is, around the bearing characteristic frequencies. The proposed method is validated by considering simulated data of a damaged bearing. Also, an experimental case is considered which focuses on collecting the vibration data issued from a run-to-failure test. It is shown that the proposed method can detect bearing defects and, as such, it appears to be an efficient tool for diagnosis purpose.

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