Extreme Learning Machine for Fault Diagnosis of Rotating Machinery

The authors present extreme learning machine (ELM) as a novel mechanism for diagnosing the faults of rotating machinery, which is reflected from the power spectrum of the vibration signals. Extreme learning machine was originally developed for the single-hidden layer feedforward neural network (SLFN) and then extended to the generalized SLFN. We obtained the fault feature table of rotating machinery by wavelet packet analysis of the power spectrum, then trained and diagnosed the fault feature table with extreme learning machine. Diagnostic results show that the extreme learning machine method achieves higher diagnostic accuracy than the probabilistic neural network (PNN) method, exhibiting superior diagnostic performance. In addition, the diagnosis of fault feature table adding noise signal indicates the extreme learning machine method provides satisfactory generalization performance.

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One-Class Classification with Extreme Learning Machine

Mathematical Problems in Engineering ◽

10.1155/2015/412957 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 33

Author(s):

Qian Leng ◽

Honggang Qi ◽

Jun Miao ◽

Wentao Zhu ◽

Guiping Su

Keyword(s):

Neural Network ◽

Extreme Learning Machine ◽

Classification Problem ◽

Traditional Learning ◽

Learning Speed ◽

One Class Classifier ◽

Learning Machine ◽

Hidden Layer ◽

One Class Classification ◽

Network Approaches

One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM). The essence of ELM is that the hidden layer need not be tuned and the output weights can be analytically determined, which leads to much faster learning speed. The experimental evaluation conducted on several real-world benchmarks shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one-class classification methods.

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Classification of catenary equipment based on a coupled genetic algorithm-extreme learning machine method

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2020.62.1.15 ◽

2020 ◽

Vol 62 (1) ◽

pp. 15-21

Author(s):

Changdong Wu

Keyword(s):

Genetic Algorithm ◽

Extreme Learning Machine ◽

Monitoring System ◽

Classification Accuracy ◽

Activation Function ◽

Machine Method ◽

Learning Machine ◽

Hidden Layer ◽

Low Dimensional

In an online monitoring system for an electrified railway, it is important to classify the catenary equipment successfully. The extreme learning machine (ELM) is an effective image classification algorithm and the genetic algorithm (GA) is a typical optimisation method. In this paper, a coupled genetic algorithm-extreme learning machine (GA-ELM) technique is proposed for the classification of catenary equipment. Firstly, the GA is used to search for optimal features by reducing the initial multi-dimensional features to low-dimensional features. Next, the optimised features are used as the input to the ELM. The ELM algorithm is then used to classify the catenary equipment. In this process, the impacts of the activation function, the number of hidden layer neurons and different models on the performance of the ELM are discussed in turn. Finally, the proposed method is compared with traditional methods in terms of classification accuracy and efficiency. Experimental results show that the number of feature dimensions decreases to 58% of the original number and the computational complexity is greatly decreased. Moreover, the reduced features and the few steps of the ELM improve the classification accuracy and speed. Noticeably, when the performance of the GA-ELM method is compared with that of the ELM method, the classification accuracy rate is 93.33% compared with 85.83% and the time consumption is 2.25 s compared with 8.85 s, respectively. That is to say, the proposed method not only decreases the number of features but also increases the classification accuracy and efficiency. This meets the needs of a real-time online condition monitoring system.

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An Effective Turkey Marble Classification System: Convolutional Neural Network with Genetic Algorithm -Wavelet Kernel - Extreme Learning Machine

Traitement du signal ◽

10.18280/ts.380434 ◽

2021 ◽

Vol 38 (4) ◽

pp. 1229-1235

Author(s):

Derya Avci ◽

Eser Sert

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Convolutional Neural Network ◽

Extreme Learning Machine ◽

Optical Illusions ◽

Factors Affecting ◽

Kernel Extreme Learning Machine ◽

Learning Machine ◽

Hidden Layer ◽

Elm Classifier

Marble is one of the most popular decorative elements. Marble quality varies depending on its vein patterns and color, which are the two most important factors affecting marble quality and class. The manual classification of marbles is likely to lead to various mistakes due to different optical illusions. However, computer vision minimizes these mistakes thanks to artificial intelligence and machine learning. The present study proposes the Convolutional Neural Network- (CNN-) with genetic algorithm- (GA) Wavelet Kernel- (WK-) Extreme Learning Machine (ELM) (CNN–GA-WK-ELM) approach. Using CNN architectures such as AlexNet, VGG-19, SqueezeNet, and ResNet-50, the proposed approach obtained 4 different feature vectors from 10 different marble images. Later, Genetic Algorithm (GA) was used to optimize adjustable parameters, i.e. k, 1, and m, and hidden layer neuron number in Wavelet Kernel (WK) – Extreme Learning Machine (ELM) and to increase the performance of ELM. Finally, 4 different feature vector parameters were optimized and classified using the WK-ELM classifier. The proposed CNN–GA-WK-ELM yielded an accuracy rate of 98.20%, 96.40%, 96.20%, and 95.60% using AlexNet, SequeezeNet, VGG-19, and ResNet-50, respectively.

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Toward deep neural networks: Mirror extreme learning machines for pattern classification

Filomat ◽

10.2298/fil2015985l ◽

2020 ◽

Vol 34 (15) ◽

pp. 4985-4996

Author(s):

Bolin Liao ◽

Chuan Ma ◽

Meiling Liao ◽

Shuai Li ◽

Zhiguan Huang

Keyword(s):

Neural Network ◽

Extreme Learning Machine ◽

Pattern Classification ◽

Simple Structure ◽

Layer Structure ◽

Feed Forward Neural Network ◽

Learning Machines ◽

S Parameter ◽

Learning Machine ◽

Hidden Layer

In this paper, a novel type of feed-forward neural network with a simple structure is proposed and investigated for pattern classification. Because the novel type of forward neural network?s parameter setting is mirrored with those of the Extreme Learning Machine (ELM), it is termed the mirror extreme learning machine (MELM). For the MELM, the input weights are determined by the pseudoinverse method analytically, while the output weights are generated randomly, which are completely different from the conventional ELM. Besides, a growing method is adopted to obtain the optimal hidden-layer structure. Finally, to evaluate the performance of the proposed MELM, abundant comparative experiments based on different real-world classification datasets are performed. Experimental results validate the high classification accuracy and good generalization performance of the proposed neural network with a simple structure in pattern classification.

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Comparison of the Extreme Learning Machine with the BP Neural Network for Short-Term Prediction of Wind Power

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.608-609.564 ◽

2012 ◽

Vol 608-609 ◽

pp. 564-568 ◽

Cited By ~ 2

Author(s):

Yi Hui Zhang ◽

He Wang ◽

Zhi Jian Hu ◽

Meng Lin Zhang ◽

Xiao Lu Gong ◽

...

Keyword(s):

Neural Network ◽

Wind Power ◽

Extreme Learning Machine ◽

Bp Neural Network ◽

Learning Algorithm ◽

Power Prediction ◽

Feed Forward Neural Network ◽

Wind Power Prediction ◽

Learning Machine ◽

Hidden Layer

Extreme learning machine (ELM) is a new and effective single-hidden layer feed forward neural network learning algorithm. Extreme learning machine only needs to set the number of hidden layer nodes of the network, and there is no need to adjust the neural network input weights and the hidden units bias, and it generates the only optimum solution, so it has the advantage of fast learning and good generalization ability. And the back propagation (BP) neural network is the most maturely applied. This paper has introduced the extreme learning machine into the wind power prediction. By comparing the wind power prediction method using the BP neural network. Study shows that the extreme learning machine has better prediction accuracy and shorter model training time.

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A Multiple Hidden Layers Extreme Learning Machine Method and Its Application

Mathematical Problems in Engineering ◽

10.1155/2017/4670187 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 9

Author(s):

Dong Xiao ◽

Beijing Li ◽

Yachun Mao

Keyword(s):

Extreme Learning Machine ◽

Least Squares Method ◽

Learning Algorithm ◽

Average Precision ◽

Machine Method ◽

Rapid Learning ◽

Actual Output ◽

Input Layer ◽

Learning Machine ◽

Hidden Layer

Extreme learning machine (ELM) is a rapid learning algorithm of the single-hidden-layer feedforward neural network, which randomly initializes the weights between the input layer and the hidden layer and the bias of hidden layer neurons and finally uses the least-squares method to calculate the weights between the hidden layer and the output layer. This paper proposes a multiple hidden layers ELM (MELM for short) which inherits the characteristics of parameters of the first hidden layer. The parameters of the remaining hidden layers are obtained by introducing a method (make the actual output zero error approach the expected hidden layer output). Based on the MELM algorithm, many experiments on regression and classification show that the MELM can achieve the satisfactory results based on average precision and good generalization performance compared to the two-hidden-layer ELM (TELM), the ELM, and some other multilayer ELM.

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Solving two-dimensional linear partial differential equations based on Chebyshev neural network with extreme learning machine algorithm

Engineering Computations ◽

10.1108/ec-08-2019-0387 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Min Liu ◽

Muzhou Hou ◽

Juan Wang ◽

Yangjin Cheng

Keyword(s):

Neural Network ◽

Partial Differential Equations ◽

Extreme Learning Machine ◽

Two Dimensional ◽

Content Type ◽

Linear Partial Differential Equations ◽

Chebyshev Neural Network ◽

Learning Machine ◽

Hidden Layer ◽

Linear Pdes

Purpose This paper aims to develop a novel algorithm and apply it to solve two-dimensional linear partial differential equations (PDEs). The proposed method is based on Chebyshev neural network and extreme learning machine (ELM) called Chebyshev extreme learning machine (Ch-ELM) method. Design/methodology/approach The network used in the proposed method is a single hidden layer feedforward neural network. The Kronecker product of two Chebyshev polynomials is used as basis function. The weights from the input layer to the hidden layer are fixed value 1. The weights from the hidden layer to the output layer can be obtained by using ELM algorithm to solve the linear equations established by PDEs and its definite conditions. Findings To verify the effectiveness of the proposed method, two-dimensional linear PDEs are selected and its numerical solutions are obtained by using the proposed method. The effectiveness of the proposed method is illustrated by comparing with the analytical solutions, and its superiority is illustrated by comparing with other existing algorithms. Originality/value Ch-ELM algorithm for solving two-dimensional linear PDEs is proposed. The algorithm has fast execution speed and high numerical accuracy.

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Predicting Surface Roughness in Turning Operation Using Extreme Learning Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.554.431 ◽

2014 ◽

Vol 554 ◽

pp. 431-435 ◽

Cited By ~ 1

Author(s):

Ahmad Nooraziah ◽

V. Janahiraman Tiagrajah

Keyword(s):

Neural Network ◽

Surface Roughness ◽

Extreme Learning Machine ◽

Cutting Parameters ◽

Feedforward Neural Network ◽

Support Vector ◽

Satisfactory Solution ◽

Modeling Techniques ◽

Learning Machine ◽

Hidden Layer

Prediction model allows the machinist to determine the values of the cutting performance before machining. According to literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Recently, Extreme Learning Machine (ELM) has been introduced as the alternative to overcome the limitation from the previous methods. ELM has similar structure as single hidden layer feedforward neural network with analytically to determine output weight. By comparing to Response Surface Methodology, Support Vector Machine and Neural Network, this paper proposed the prediction of surface roughness using ELM method. The result indicates that ELM can yield satisfactory solution for predicting surface roughness in term of training speed and parameter selection.

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Moving Object Detection and Tracking using Genetic Algorithm Enabled Extreme Learning Machine

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2018.2.3064 ◽

2018 ◽

Vol 13 (2) ◽

pp. 162-174 ◽

Cited By ~ 1

Author(s):

G. Jemilda ◽

S. Baulkani

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Extreme Learning Machine ◽

Probabilistic Neural Network ◽

Moving Object ◽

Support Vector ◽

Feed Forward Neural Network ◽

Shape Information ◽

Speeded Up Robust Features ◽

Learning Machine

In this proposed work, the moving object is localized using curvelet transform, soft thresholding and frame differencing. The feature extraction techniques are applied on to the localized object and the texture, color and shape information of objects are considered. To extract the shape information, Speeded Up Robust Features (SURF) is used. To extract the texture features, the Enhanced Local Vector Pattern (ELVP) and to extract color features, Histogram of Gradient (HOG) are used and then reduced feature set obtained using genetic algorithm are fused to form a single feature vector and given into the Extreme Learning Machine (ELM) to classify the objects. The performance of the proposed work is compared with Naive Bayes, Support Vector Machine, Feed Forward Neural Network and Probabilistic Neural Network and inferred that the proposed method performs better.

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