scholarly journals Fault diagnosis of roller bearings using selected classifiers

2018 ◽  
Vol 19 (12) ◽  
pp. 597-601
Author(s):  
Jakub Piekoszewski
Keyword(s):  
Neural Network ◽  
Roller Bearing ◽  
Fault Classification ◽  
Support Vector ◽  
Analysis Software ◽  
Feed Forward Neural Network ◽  
Roller Bearings ◽  
The University ◽  
Bearing Damage ◽  
Selection Of

Minor roller bearing damage may lead to serious failures of the de-vice. Thus, it is very important to detect such damage as early as possible to prevent further damage. This paper presents a selection of several theoretical tools from the field of artificial intelligence and their application in roller bearings fault classification. The considered tools are: k-nearest neighbour algorithm, decision tree, support vector machine, feed forward neural network (multilayer perceptron), Bayesian network and neural network with radial basis functions. All numerical experiments presented in the paper were performed with the use of real-world dataset and WEKA (Waikato Environment for Knowledge Analysis) software, available at the server of the University of Waikato.

scholarly journals Fault Classification of Axial and Radial Roller Bearings Using Transfer Learning through a Pretrained Convolutional Neural Network

Designs ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 56 ◽  
Author(s):  
Martin Hemmer ◽  
Huynh Van Khang ◽  
Kjell Robbersmyr ◽  
Tor Waag ◽  
Thomas Meyer
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Fault Classification ◽  
Support Vector ◽  
Frequency Spectra ◽  
Characteristic Frequencies ◽  
Roller Bearings

Detecting bearing faults is very important in preventing non-scheduled shutdowns, catastrophic failures, and production losses. Localized faults on bearings are normally detected based on characteristic frequencies associated with faults in time and frequency spectra. However, missing such characteristic frequency harmonics in a spectrum does not guarantee that a bearing is healthy, or noise might produce harmonics at characteristic frequencies in the healthy case. Further, some defects on roller bearings could not produce characteristic frequencies. To avoid misclassification, bearing defects can be detected via machine learning algorithms, namely convolutional neural network (CNN), support vector machine (SVM), and sparse autoencoder-based SVM (SAE-SVM). Within this framework, three fault classifiers based on CNN, SVM, and SAE-SVM utilizing transfer learning are proposed. Transfer of knowledge is achieved by extracting features from a CNN pretrained on data from the imageNet database to classify faults in roller bearings. The effectiveness of the proposed method is investigated based on vibration and acoustic emission signal datasets from roller bearings with artificial damage. Finally, the accuracy and robustness of the fault classifiers are evaluated at different amounts of noise and training data.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

scholarly journals A Proposed Heuristic Optimization Algorithm for Detecting Network Attacks

2019 ◽  
Vol 2 (4) ◽  
pp. 530
Author(s):  
Amr Hassan Yassin ◽  
Hany Hamdy Hussien
Keyword(s):  
Neural Network ◽  
Heuristic Algorithms ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Network Attacks ◽  
Security Issues ◽  
Proposed Model ◽  
Network Intrusions ◽  
Attack Patterns ◽  
Selection Of

Due to the exponential growth of E-Business and computing capabilities over the web for a pay-for-use groundwork, the risk factors regarding security issues also increase rapidly. As the usage increases, it becomes very difficult to identify malicious attacks since the attack patterns change. Therefore, host machines in the network must continually be monitored for intrusions since they are the final endpoint of any network. The purpose of this work is to introduce a generalized neural network model that has the ability to detect network intrusions. Two recent heuristic algorithms inspired by the behavior of natural phenomena, namely, the particle swarm optimization (PSO) and gravitational search (GSA) algorithms are introduced. These algorithms are combined together to train a feed forward neural network (FNN) for the purpose of utilizing the effectiveness of these algorithms to reduce the problems of getting stuck in local minima and the time-consuming convergence rate. Dimension reduction focuses on using information obtained from NSL-KDD Cup 99 data set for the selection of some features to discover the type of attacks. Detecting the network attacks and the performance of the proposed model are evaluated under different patterns of network data.

scholarly journals Fault Diagnosis of Transmission Line using Feed Forward Neural Network

2019 ◽  
Vol 8 (9S2) ◽  
pp. 263-268
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Transmission Lines ◽  
Field Work ◽  
Neural Nets ◽  
Feed Forward Neural Network ◽  
Training Time ◽  
Optimum Result ◽  
Human Effort ◽  
Selection Of

The implementation of neural network for the fault diagnosis is to improve the dependability of the proposed scheme by providing a more accurate, faster diagnosis relaying scheme as compared with the conventional relaying schemes. It is important to improve the relaying schemes regarding the shortcoming of the system and increase the dependability of the system by using the proposed relaying scheme. It also provide more accurate, faster relaying scheme. It also gives selective schemes as compared to conventional system. The techniques for survey employed some methods for the collection of data which involved a literature review of journals, from review on books, newspaper, magazines as well as field work, additional data was collected from researchers who are working in this field. To achieve optimum result we have to improve following things: (i) Training time, (ii) Selection of training vector, (iii) Upgrading of trained neural nets and integration of technologies. AI with its promise of adaptive training and generalization deserves scope. As a result we obtain a system which is more reliable, more accurate, and faster, has more dependability as well as it will selective according to the proposed relaying scheme as compare to the conventional relaying scheme. This system helps us to reduce the shortcoming like major faults which we faced in the complex system of transmission lines which will helps in reducing human effort, saves cost for maintaining the transmission system.

scholarly journals An Indoor and Outdoor Positioning Using a Hybrid of Support Vector Machine and Deep Neural Network Algorithms

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Abebe Belay Adege ◽  
Hsin-Piao Lin ◽  
Getaneh Berie Tarekegn ◽  
Yirga Yayeh Munaye ◽  
Lei Yen
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Deep Neural Network ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Online Data ◽  
Location Services ◽  
Outdoor Environments ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

Indoor and outdoor positioning lets to offer universal location services in industry and academia. Wi-Fi and Global Positioning System (GPS) are the promising technologies for indoor and outdoor positioning, respectively. However, Wi-Fi-based positioning is less accurate due to the vigorous changes of environments and shadowing effects. GPS-based positioning is also characterized by much cost, highly susceptible to the physical layouts of equipment, power-hungry, and sensitive to occlusion. In this paper, we propose a hybrid of support vector machine (SVM) and deep neural network (DNN) to develop scalable and accurate positioning in Wi-Fi-based indoor and outdoor environments. In the positioning processes, we primarily construct real datasets from indoor and outdoor Wi-Fi-based environments. Secondly, we apply linear discriminate analysis (LDA) to construct a projected vector that uses to reduce features without affecting information contents. Thirdly, we construct a model for positioning through the integration of SVM and DNN. Fourthly, we use online datasets from unknown locations and check the missed radio signal strength (RSS) values using the feed-forward neural network (FFNN) algorithm to fill the missed values. Fifthly, we project the online data through an LDA-based projected vector. Finally, we test the positioning accuracies and scalabilities of a model created from a hybrid of SVM and DNN. The whole processes are implemented using Python 3.6 programming language in the TensorFlow framework. The proposed method provides accurate and scalable positioning services in different scenarios. The results also show that our proposed approach can provide scalable positioning, and 100% of the estimation accuracies are with errors less than 1 m and 1.9 m for indoor and outdoor positioning, respectively.

On the Detectability of Roller Bearing Damage by Frequency Analysis

1993 ◽  
Vol 207 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Y-T Su ◽  
Y-T Sheen
Keyword(s):  
Frequency Analysis ◽  
Roller Bearing ◽  
Vibration Spectra ◽  
Frequency Information ◽  
Roller Bearings ◽  
Bearing Damage

This study investigates the detectability of roller bearing damage by the frequency analysis of bearing vibrations. The magnitude characteristics of peaks in the vibration spectra are analysed. It is shown that the frequency information of vibration spectra for undamaged roller bearings can be the same as that for damaged ones. However, the magnitude information of vibration spectra for undamaged roller bearings is different from that for damaged ones. Thus, to detect the initial fault of roller bearing reliably, both the frequency information and magnitude information of vibration spectra have to be used.

Roller Bearing Fault Diagnosis Based on IMF Kurtosis and SVM

2013 ◽  
Vol 694-697 ◽  
pp. 1160-1166
Author(s):  
Ke Heng Zhu ◽  
Xi Geng Song ◽  
Dong Xin Xue
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Fault Classification ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Bearing Vibration ◽  
The Imf

This paper presents a fault diagnosis method of roller bearings based on intrinsic mode function (IMF) kurtosis and support vector machine (SVM). In order to improve the performance of kurtosis under strong levels of background noise, the empirical mode decomposition (EMD) method is used to decompose the bearing vibration signals into a number of IMFs. The IMF kurtosis is then calculated because of its sensitivity of impulses caused by faults. Subsequently, the IMF kurtosis values are treated as fault feature vectors and input into SVM for fault classification. The experimental results show the effectiveness of the proposed approach in roller bearing fault diagnosis.

A NOVEL APPROACH FOR SHORT-TERM LOAD FORECASTING USING SUPPORT VECTOR MACHINES

2004 ◽  
Vol 14 (05) ◽  
pp. 329-335 ◽  
Author(s):  
LIANG TIAN ◽  
AFZEL NOORE
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Modeling Approach ◽  
Novel Approach ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Network Approaches

A support vector machine (SVM) modeling approach for short-term load forecasting is proposed. The SVM learning scheme is applied to the power load data, forcing the network to learn the inherent internal temporal property of power load sequence. We also study the performance when other related input variables such as temperature and humidity are considered. The performance of our proposed SVM modeling approach has been tested and compared with feed-forward neural network and cosine radial basis function neural network approaches. Numerical results show that the SVM approach yields better generalization capability and lower prediction error compared to those neural network approaches.

Multiclass Contour-Preserving Classification with Support Vector Machine (SVM)

2017 ◽  
Vol 26 (2) ◽  
pp. 323-334 ◽  
Author(s):  
Piyabute Fuangkhon
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
University Of California ◽  
Support Vector ◽  
Data Sets ◽  
Feed Forward Neural Network ◽  
Real World Data ◽  
Data Set ◽  
The University ◽  
Training Sets

AbstractMulticlass contour-preserving classification (MCOV) has been used to preserve the contour of the data set and improve the classification accuracy of a feed-forward neural network. It synthesizes two types of new instances, called fundamental multiclass outpost vector (FMCOV) and additional multiclass outpost vector (AMCOV), in the middle of the decision boundary between consecutive classes of data. This paper presents a comparison on the generalization of an inclusion of FMCOVs, AMCOVs, and both MCOVs on the final training sets with support vector machine (SVM). The experiments were carried out using MATLAB R2015a and LIBSVM v3.20 on seven types of the final training sets generated from each of the synthetic and real-world data sets from the University of California Irvine machine learning repository and the ELENA project. The experimental results confirm that an inclusion of FMCOVs on the final training sets having raw data can improve the SVM classification accuracy significantly.

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