ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES

Bearings are essential components in the most electrical equipment. Procedures for monitoring the condition of bearings must be developed to prevent unexpected failure of these components during operation to avoid costly consequences. In this paper, the design of a monitoring system for the detection of rolling element-bearings failure is proposed. The method for detecting and locating this type of fault is carried out using advanced intelligent techniques based on a Perceptron Multilayer Artificial Neural Network (MLP-ANN); its database uses statistical indicators characterizing vibration signals. The effectiveness of the proposed method is illustrated using experimentally obtained bearing vibration data, and the results have shown good accuracy in detecting and locating defects.

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Automatic Clustering of Rolling Element Bearings Defects with Artificial Neural Network

10.1063/1.2216674 ◽

2006 ◽

Author(s):

M. Antonini

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Rolling Element Bearings ◽

Automatic Clustering ◽

Rolling Element ◽

Artificial Neural

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Prediction of defect size in rolling element bearings using acoustic emission signals with an artificial neural network

International Journal of Condition Monitoring ◽

10.1784/204764299455315263 ◽

2016 ◽

Vol 6 (2) ◽

pp. 38-46

Author(s):

V Vital Rao ◽

Ch Ratnam

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Acoustic Emission ◽

Diagnostic Tool ◽

Vibration Amplitude ◽

Defect Size ◽

Rolling Element Bearings ◽

Rolling Element ◽

Artificial Neural ◽

Amplitude Level

In the condition monitoring of rotating machinery, vibration analysis of rolling element bearings is a popular diagnostic tool even though the vibration signals caused by bearing defects are distorted by other faults and mechanical noise, particularly in a hostile environment. The acoustic emission (AE) method is a non-destructive testing (NDT) technique used in structural health monitoring and its application for bearing diagnosis is gaining momentum as an alternative diagnostic tool due to its inherent high signal-to-noise (SNR) ratio. In earlier studies, although the researchers focused on different types of seeded defects, with random shape and size, and measured their vibration amplitude, they could not ascertain the correlation between the defect size and its respective vibration amplitude. A bearing test-rig was designed and established to study the various size defects in rolling element bearings. The experimental investigation reported in this paper predicts the bearing damage severity with respect to the AE amplitude level, using the artificial neural network (ANN) technique. This experiment includes seeded defects of various sizes, ie gradual increase of defect width on the outer race of radially-loaded cylindrical roller bearings at different parameters, and the data acquired through an acoustic emission probe. Experimental data was imported to the ANN, in which a multilayer perception model was used with a back-propagation algorithm using the input parameters of load, r/min and AE amplitude level and defect size as the output. The predicted defect sizes are compared with the actual seeded defect sizes and the percentage error was minimal. In this paper, an attempt has been made to predict the defect size with the help of AE and ANN techniques.

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Performance evaluation of pattern recognition networks using electromyography signal and time-domain features for the classification of hand gestures

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

10.1177/0954411920912119 ◽

2020 ◽

Vol 234 (6) ◽

pp. 639-648

Author(s):

S Mary Vasanthi ◽

T Jayasree

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Time Domain ◽

Support Vector ◽

Finger Movement ◽

Finger Movements ◽

Feed Forward ◽

Artificial Neural ◽

Deep Learning Neural Network

The problem of classifying individual finger movements of one hand is focused in this article. The input electromyography signal is processed and eight time-domain features are extracted for classifying hand gestures. The classified finger movements are thumb, middle, index, little, ring, hand close, thumb index, thumb ring, thumb little and thumb middle and the hand grasps are palmar class, spherical class, hook class, cylindrical class, tip class and lateral class. Four state-of-the-art classifiers namely feed forward artificial neural network, cascaded feed forward artificial neural network, deep learning neural network and support vector machine are selected for this work to classify the finger movements and hand grasps using the extracted time-domain features. The experimental results show that the artificial neural network classifier is stabilized at 6 epochs for finger movement dataset and at 4 epochs for hand grasps dataset with low mean square error. However, the support vector machine classifier attains the maximum accuracy of 97.3077% for finger movement dataset and 98.875% for hand grasp dataset which is significantly greater than feed forward artificial neural network, cascaded feed forward artificial neural network and deep learning neural network classifiers.

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