scholarly journals Comparison between Artificial Neural Network and Support Vector Method for a Fault Diagnostics in Rolling Element Bearings

2016 ◽  
Vol 144 ◽  
pp. 390-397 ◽  
Author(s):  
J.P. Patel ◽  
S.H. Upadhyay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector ◽  
Rolling Element Bearings ◽  
Fault Diagnostics ◽  
Vector Method ◽  
Rolling Element ◽  
Artificial Neural ◽  
Support Vector Method

scholarly journals Fault diagnosis of rolling element bearings using artificial neural network

2020 ◽  
Vol 10 (5) ◽  
pp. 5288
Author(s):  
Saadi Laribi Souad ◽  
Bendiabdellah Azzedine ◽  
Samir Meradi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Equipment ◽  
Rolling Element Bearings ◽  
Vibration Data ◽  
Essential Components ◽  
Rolling Element ◽  
Bearing Vibration ◽  
Artificial Neural ◽  
Statistical Indicators

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.

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

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.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

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