Diagnosis of Rolling Element Bearing Faults Using Radial Basis Function Networks

Jack, L.B.; Nandi, Asoke K.; McCormick, A.C.

doi:10.1007/s005290050031

Cited by 37 publications

(16 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Beyond ANNs and SVMs, Fuzzy approaches [14], hybrid systems, like Adaptive Neuro-Fuzzy Inference System (ANFIS) [11,4], multi-layer feed-forward [5], radial basis function [8], wavelet neural networks [13], adaptive resonance theory networks are widely applied. Patter recognition models [15], automated fuzzy inference [10] and genetic algorithms [9] are further applied to assist automatic bearing inspection.…”

Section: Image Processing In Detection Of Bearing Defectsmentioning

confidence: 99%

Application of machine vision in manufacturing of bearings using ANN and SVM

Deák

Kocsis

Vámosi

2015

Proceedings of the 9th International Conference on Applied Informatics, Volume 1

View full text Add to dashboard Cite

Bearings have a vital role in nearly all rotating machines. Making adequate bearings is very important that satisfy all needs which emerge both in manufacturing and during operation. In former times bearings were examined by only humans, however human inspection is instable and time consuming. In this article, we are investigating a machine learning system that could make more accurate measurements regarding geometry, shape, color, surface defects, deformations and other failures by image acquisition. To achive higher resolution, magnifying of the surface with optical microscopes and scanning electron microscope (SEM) is inevitable. With these methods even tiny failures can be detected. Machine learning methods have beeen developed such as artificial neural networks (ANN) and support vector machines (SVM). Bearing manufacturing failures, image processing techniques are presented in this article besides artificial neural network system that can percieve manufacturing defects approximately 90% efficiency according to our experiments. Recent research is connected to a manufacturing of bearings in a real company in Hungary.

show abstract

Section: Image Processing In Detection Of Bearing Defectsmentioning

confidence: 99%

Application of machine vision in manufacturing of bearings using ANN and SVM

Deák

Kocsis

Vámosi

2015

Proceedings of the 9th International Conference on Applied Informatics, Volume 1

View full text Add to dashboard Cite

show abstract

“…Most of the bearings monitoring schemes are focused on bearing localized defects, because they allow an enough reliable frequency analysis. On the contrary, the generalized roughness faults, for example, produce unpredictable broadband effects in the machine The bearing faults produce a variation of the vibration mode in the machine, and statistical features from time [9] [10], as RMS, crest factor, standard deviation, rectified skew and others are useful to exhibit the system vibration condition. However, the fault effects in the acquired signal will be attenuated as the vibration is acquired further from the origin of the fault.…”

Section: Application To Bearing Fault Diagnosismentioning

confidence: 99%

Bearing fault diagnosis by EXIN CCA

Cirrincione

Henao

Delgado

et al. 2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

Abstract-EXIN CCA is an extension of the Curvilinear Component Analysis (CCA), which solves for the noninvariant CCA projection and allows representing data drawn under different operating conditions. It can be applied to data visualization, interpretation (as a kind of sensor of the underlying physical phenomenon) and classification for real time industrial applications. Here an example is given for bearing fault diagnostics in an electromechanical device.

show abstract

“…The applications of ANNs are mainly in the areas of machine learning, computer vision and pattern recognition because of their high accuracy and good generalization capability [11][12][13][14][15][16][17][18]. Though in the area of machine condition monitoring, MLPs are being used for quite some time, the applications of RBFs and PNNs are relatively recent [3,[19][20][21]. In [21], a procedure was presented for condition monitoring of rolling element bearings comparing the performance of these ANNs, with all calculated signal features and fixed parameters for the classifiers.…”

mentioning

confidence: 99%

“…In [22], a GA based approach was introduced for selection of input features and number of neurons in the hidden layer. The features were extracted from the entire signal under each condition and operating speed [19,21]. In [23], some preliminary results of MLPs and GAs were presented for fault detection of gears using only the time domain features of vibration signals.…”

mentioning

confidence: 99%

Artificial neural networks and genetic algorithm for bearing fault detection

2005

View full text Add to dashboard Cite

A study is presented to compare the performance of three types of artificial neural network (ANN), namely, multi layer perceptron (MLP), radial basis function (RBF) network and probabilistic neural network (PNN), for bearing fault detection. Features are extracted from time domain vibration signals, without and with preprocessing, of a rotating machine with normal and defective bearings. The extracted features are used as inputs to all three ANN classifiers: MLP, RBF and PNN for two-class (normal or fault) recognition. Genetic algorithms (GAs) have been used to select the characteristic parameters of the classifiers and the input features. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine. The roles of different vibration signals and preprocessing techniques are investigated. The results show the effectiveness of the features and the classifiers in detection of machine condition.

show abstract

Diagnosis of Rolling Element Bearing Faults Using Radial Basis Function Networks

Cited by 37 publications

References 0 publications

Application of machine vision in manufacturing of bearings using ANN and SVM

Application of machine vision in manufacturing of bearings using ANN and SVM

Bearing fault diagnosis by EXIN CCA

Artificial neural networks and genetic algorithm for bearing fault detection

Contact Info

Product

Resources

About