scholarly journals End of the Assembly Line Gearbox Fault Inspection Using Artificial Neural Network and Support Vector Machines

2019 ◽  
Vol 24 (No 1) ◽  
pp. 68-84
Author(s):  
Prasad V. Kane ◽  
Atul B. Andhare
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Assembly Line ◽  
Test Bench ◽  
Fault Classification ◽  
Support Vector ◽  
Statistical Features ◽  
Vibration Signals ◽  
Artificial Neural ◽  
Better Than

Gear fault diagnosis is important not only during the routine maintenance of machinery, but also during the inspection of newly manufactured gearboxes at the end of the assembly line. This paper discusses the application of an artificial neural network (ANN) and a support vector machine (SVM) for identifying faults in the gearbox, using the psychoacoustic and conventional statistical features extracted from acoustics and vibration signals. It is observed that at the end of the assembly line, the gearbox is tested by mounting it on a test bench and driving it by an electric motor. Based on the sound emitted while running on the test bench, the operator decides on the acceptance of the gearbox for further assembly on a vehicle or machine. This method of acceptance or rejection of the gearbox involves subjectivity and it is not reliable. Hence, it is important to have a reliable and objective fault detection and diagnosis method. To eliminate subjectivity, psychoacoustic features, which are derived from the science of listening in human beings, are proposed to be used as features, along with ANN and SVMs as classifiers. To ascertain the ability of the psychoacoustic features to classify faults, laboratory experiments are carried on a test setup by simulating faults like a gear shaft misalignment, a profile error of a gear tooth, a crack at the root of the tooth, and a broken tooth. ANN and SVM are trained with the psychoacoustic features extracted from the acoustic signal and other statistical features from the acoustics and vibration signals. The trained SVM and ANN are tested for fault classification for these features and their accuracy is compared. Fault classification accuracy is found to be 95.65% for ANN and 93.44% for SVM with psychoacoustic features and is found to be better than pure statistical features obtained from the vibration and acoustic signals. With the optimised ANN and SVM architecture, SVM is found to be performing better than ANN. It is concluded that the psychoacoustic features, along with the ANN and SVM method, could be adopted at the end of assembly line inspection to make the inspection process more objective.

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.

Artificial neural network-based seismic detector

1995 ◽  
Vol 85 (1) ◽  
pp. 308-319 ◽  
Author(s):  
Jin Wang ◽  
Ta-Liang Teng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Input Data ◽  
Seismic Event ◽  
Signal To Noise Ratio ◽  
Type B ◽  
Earthquake Detection ◽  
Seismic Event Detection ◽  
Artificial Neural ◽  
Better Than

Abstract An artificial neural network-based pattern classification system is applied to seismic event detection. We have designed two types of Artificial Neural Detector (AND) for real-time earthquake detection. Type A artificial neural detector (AND-A) uses the recursive STA/LTA time series as input data, and type B (AND-B) uses moving window spectrograms as input data to detect earthquake signals. The two AND's are trained under supervised learning by using a set of seismic recordings, and then the trained AND's are applied to another set of recordings for testing. Results show that the accuracy of the artificial neural network-based seismic detectors is better than that of the conventional algorithms solely based on the STA/LTA threshold. This is especially true for signals with either low signal-to-noise ratio or spikelike noises.

Analysis of Stock Volatility Clustering Using ANN

2015 ◽  
Vol 28 (2) ◽  
pp. 32-45 ◽  
Author(s):  
Manish Kumar ◽  
Santanu Das ◽  
Sneha Govil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Building ◽  
Stochastic Volatility Model ◽  
Propagation Model ◽  
Stock Index ◽  
Support Vector ◽  
Stock Volatility ◽  
Volatility Model ◽  
Artificial Neural

The model building theories broadly categorize the stock index forecasting models into two broad categories: Based on statistical theory consisting models such as Stochastic Volatility model (SV) and General Autoregressive Conditional Heteroskedasticity (GARCH) whereas other one based on artificial intelligence based models, such as artificial neural network (ANN), the support vector machine (SVM) and technique used for optimization such as particle swarm optimization (PSO). In existing literature, many of the statistical models when compared with artificial neural network models were outperformed by these models. This paper analyses stock volatility using ANN models as Multilayer perceptron with back propagation model and Radial Basis function.

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