Diagnosis of multiple faults using a double parallel two-hidden-layer extreme learning machine

Extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feedforward neural networks (SLFNs), provides efficient unified learning solutions for the applications of feature learning, clustering, regression and classification. Different from the common understanding and tenet that hidden neurons of neural networks need to be iteratively adjusted during training stage, ELM theories show that hidden neurons are important but need not be iteratively tuned. In fact, all the parameters of hidden nodes can be independent of training samples and randomly generated according to any continuous probability distribution. And the obtained ELM networks satisfy universal approximation and classification capability. The fully connected ELM architecture has been extensively studied. However, ELM with local connections has not attracted much research attention yet. This paper studies the general architecture of locally connected ELM, showing that: 1) ELM theories are naturally valid for local connections, thus introducing local receptive fields to the input layer; 2) each hidden node in ELM can be a combination of several hidden nodes (a subnetwork), which is also consistent with ELM theories. ELM theories may shed a light on the research of different local receptive fields including true biological receptive fields of which the exact shapes and formula may be unknown to human beings. As a specific example of such general architectures, random convolutional nodes and a pooling structure are implemented in this paper. Experimental results on the NORB dataset, a benchmark for object recognition, show that compared with conventional deep learning solutions, the proposed local receptive fields based ELM (ELM-LRF) reduces the error rate from 6.5% to 2.7% and increases the learning speed up to 200 times.

ObjectiveCardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate variability (HRV) signals. This approach is based on subspaces decomposition of HRV signals using multiscale wavelet packet (MSWP) transform and entropy features extracted from decomposed HRV signals. The detection performance was analyzed using Fisher ranking method, generalized discriminant analysis (GDA) and binary classifier as extreme learning machine (ELM). The ranking strategies designate rank to the available features extracted by entropy methods from decomposed heart rate variability (HRV) signals and organize them according to their clinical importance. The GDA diminishes the dimension of ranked features. In addition, it can enhance the classification accuracy by picking the best discerning of ranked features. The main advantage of ELM is that the hidden layer does not require tuning and it also has a fast rate of detection.MethodologyFor the detection of CAD patients, the HRV data of healthy normal sinus rhythm (NSR) and CAD patients were obtained from a standard database. Self recorded data as normal sinus rhythm (Self_NSR) of healthy subjects were also used in this work. Initially, the HRV time-series was decomposed to 4 levels using MSWP transform. Sixty two features were extracted from decomposed HRV signals by non-linear methods for HRV analysis, fuzzy entropy (FZE) and Kraskov nearest neighbour entropy (K-NNE). Out of sixty-two features, 31 entropy features were extracted by FZE and 31 entropy features were extracted by K-NNE method. These features were selected since every feature has a different physical premise and in this manner concentrates and uses HRV signals information in an assorted technique. Out of 62 features, top ten features were selected, ranked by a ranking method called as Fisher score. The top ten features were applied to the proposed model, GDA with Gaussian or RBF kernal + ELM having hidden node as sigmoid or multiquadric. The GDA method transforms top ten features to only one feature and ELM has been used for classification.ResultsNumerical experimentations were performed on the combination of datasets as NSR-CAD and Self_NSR- CAD subjects. The proposed approach has shown better performance using top ten ranked entropy features. The GDA with RBF kernel + ELM having hidden node as multiquadric method and GDA with Gaussian kernel + ELM having hidden node as sigmoid or multiquadric method achieved an approximate detection accuracy of 100% compared to ELM and linear discriminant analysis (LDA)+ELM for both datasets. The subspaces level-4 and level-3 decomposition of HRV signals by MSWP transform can be used for detection and analysis of CAD patients.

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.

In order to improve the stability of extreme learning machine (ELM), it was proposed to use the combination of robust independent component analysis (RobustICA) and particle swarm optimization ELM algorithm. It was applied to identify the maturity of cuiguan pear at different growing periods. First the two order derivative and the discrete wavelet transform were adopted to remove the background noise and compression spectra. Then RobustICA was used to decompose the spectra to extract independent components and mixed coefficient matrices. Finally, by using particle swarm optimization, the best input layer weight and hidden layer deviation were obtained and adjusted the number of hidden layer nodes to get the best ELM model to differentiate maturity. The method was used to identify the maturity of cuiguan pear at four different stages, and has strong robustness. The results showed that RobustICA-PSO-ELM had better discrimination ability compared with RobustICA - ELM and ELM, and the accuracy increases from 75% to 94.4%.

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

An improved classification approach is proposed to solve the hot research problem of some complex multiclassification samples based on extreme learning machine (ELM). ELM was proposed based on the single‐hidden layer feed‐forward neural network (SLFNN). ELM is characterized by the easier parameter selection rules, the faster converge speed, the less human intervention, and so on. In order to further improve the classification precision of ELM, an improved generation method of the network structure of ELM is developed by dynamically adjusting the number of hidden nodes. The number change of the hidden nodes can serve as the computational updated step length of the ELM algorithm. In this paper, the improved algorithm can be called the variable step incremental extreme learning machine (VSI‐ELM). In order to verify the effect of the hidden layer nodes on the performance of ELM, an open‐source machine learning database (University of California, Irvine (UCI)) is provided by the performance test data sets. The regression and classification experiments are used to study the performance of the VSI‐ELM model, respectively. The experimental results show that the VSI‐ELM algorithm is valid. The classification of different degrees of broken wires is now still a problem in the nondestructive testing of hoisting wire rope. The magnetic flux leakage (MFL) method of wire rope is an efficient nondestructive method which plays an important role in safety evaluation. Identifying the proposed VSI‐ELM model is effective and reliable for actually applying data, and it is used to identify the classification problem of different types of samples from MFL signals. The final experimental results show that the VSI‐ELM algorithm is of faster classification speed and higher classification accuracy of different broken wires.

The shallow seawater depth inversion based on remote sensing technology is important for water depth detection, which is of considerable significance to marine engineering, shipping, and marine military security. In this study, we took the Taiping Island and its adjacent waters in the South China Sea as a test bed and developed a water depth inversion model on the basis of extreme learning machine (ELM) and extreme learning machine optimized by genetic algorithm (GA-ELM). In GA-ELM, the input weights and the hidden layer biases were optimized by genetic algorithm. The two models allowed the evaluation of nonlinear relationships between the reflectance of high-resolution imagery from WorldView-2 and actual water depth obtained from the S-57 sea chart. The eight bands of the high-resolution image and the actual water depth were used as the input layer and the output layer, and the sigmoid function was introduced as activation function. Finally, the model accuracy was evaluated by using mean relative error (MRE), root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and the regression analysis between the retrieved water depth and the actual data. The simulation results showed that the two models had better stability than the second-order polynomial regression, BP neural network, and RBF neural network. Furthermore, GA-ELM had a more compact network structure and better generalization ability than ELM. Thus, we concluded that GA-ELM had higher precision and could achieve a better inversion result in the experimental area.

In this paper, we have proposed a new algorithm to train neural network, called Class-Constrained Extreme Learning Machine (C\(^{2}\)ELM), which is based on Extreme Learning Machine (ELM). In C\(^{2}\)ELM, we use class information to constrain different parts of connection weights between input layer and hidden layer using Extreme Learning Machine Auto Encoder (ELM-AE). In this way, we add class information to the connection weights and make the features in the hidden layer which are learned from input space be more discriminative than other methods based on ELM. Meanwhile, C\(^{2}\)ELM can retain the advantages of ELM. The experiments shown that C\(^{2}\)ELM is effective and efficient and can achieve a higher performance in contrast to other ELM based methods.

Extreme learning machine (ELM) with a hidden layer has been successfully applied in many fields. In order to enhance the generalization performance of ELM, an enhanced ELM with a double parallel structure (DP-ELM) was proposed. In the proposed DP-ELM, a special connection between the input nodes and the output nodes was built. With the connection between the hidden nodes and output nodes, the output nodes of DP-ELM can receive the information from hidden nodes. With the special connection, the self-information from inputs can be directly passed to the output nodes. To verify the performance of the proposed DP-ELM, simulations are carried out using 5 regression datasets, including two process modeling applications. Simulation results show that, compared with ELM, the proposed DP-ELM could achieve higher accuracy better stable ability.

Sewage treatment process has the following characteristics: nonlinear, delay etc, and is very complicated to establish the model for its control process. A reasonable model is set up for elaborate prediction effluent quality, which can satisfy the standard of the effluent water and requirements of energy saving simultaneously. Extreme learning machine (ELM), i.e the machine learning method that new lately developed has high accuracy, reliability and outstanding performance in prediction. To get higher prediction effect, in this paper, there are two ways are proposed to improve the ELM, (1) Optimizing the parameters. The ELM whose input weights and bias threshold are optimized by particle swarm optimization algorithm (PSO) and genetic algorithm (GA), respectively; (2) Changing learning mode. To develop an online sequential learning algorithm (OS) for the ELM with additive or radial basis function (RBF) hidden nodes in a unified framework. Therefore, the several comparison approaches refer to optimize the ELM, e.g., PSO-ELM, GA-ELM, OS-ELM are applied to effluent quality prediction, and chemical oxygen demand (COD) is taken as examples in this paper. The results show that PSO-ELM model has remarkably superior performance on effluent quality prediction than peer models in terms of mean absolute error, mean absolute percentage error, root mean square error, and coefficient of determination.

This paper presents a practical and low cost design-for-testability (DFT) scheme for the row-based field programmable gate array (FPGA) which is widely used for rapid prototyping, hardware verification/emulation of VLSI chips and manufacturing of complex digital systems. A new module is introduced for the DFT of the FPGA. The proposed DFT scheme permits the uncommitted FPGA to be tested using a set of constant cardinality (C-testability) for single and multiple stuck-at fault detection, while reducing the number of required primary test pins to only one. The number of tests for the FPGA is still 8+n/sub f/ (where n/sub f/ is the number of sequential modules in a row of the array), but only one primary pin and a small amount of testing circuitry are now required. This paper also modifies the single fault test set to accomplish multiple fault detection under two multiple fault models: the multiple fault single module (MFSM) and the single fault multiple module (SFMM) models. It is shown that by appropriately changing the don't care entries in the vectors of the test set for single fault detection, 100% and nearly 100% fault coverages can be achieved under the MFSM and SFMM models respectively.

This paper proposes a novel artificial neural network called Parallel Layer Perceptron Fast Learning Network (PLP-FLN). In PLP-FLN, a parallel single hidden layer feed-forward neural network is added on the basis of Fast Learning Network (FLN) which is an improved Extreme Learning Machine (ELM). Input weights and hidden layer biases are randomly generated. The weights connect the output nodes and the input nodes, and the weights connect the output nodes and the hidden nodes are analytically determined based on least squares methods. In order to test the PLP-FLN validity, this paper compared it with ELM, FLN, Kernel ELM and Incremental ELM through 12 regression applications and 7 classification problems. By comparing the experimental results, it shows that the PLP-FLN with much more compact networks have demonstrated better approximations, classification performances and generalization ability.

The extreme learning machine (ELM), which was originally proposed for “generalized” single-hidden layer feedforward neural networks (SLFNs), provides efficient unified learning solutions for the applications of regression and classification. It presents competitive accuracy with superb efficiency in many applications. However, due to its single-layer architecture, feature selection using ELM may not be effective for natural signals. To address this issue, this paper proposes a new ELM-based multi-layer learning framework for dimension reduction. The novelties of this work are as follows: (1) Unlike the existing multi-layer ELM methods in which all hidden nodes are generated randomly, in this paper some hidden layers are calculated by replacement technologies. By doing so, more important information can be exploited for feature learning, which lead to a better generalization performance. (2) Unlike the existing multi-layer ELM methods which only work for sparse representation, the proposed method is designed for dimension reduction. Experimental results on several classification datsets show that, compared to other feature selection methods, the proposed method performs competitively or much better than other feature selection methods with fast learning speed.

Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.

Multiple fault diagnosis is a challenging problem because the number of candidates grows exponentially in the number of faults. The multiple fault problems is important, since the single fault assumption can lead to incorrect or failed diagnoses when multiple faults occur. In this work, we present an approach for diagnosing multi faults based on model using techniques of detection and localization. We use an observer to generate residuals for a decision in a stage of monitoring and diagnostic system when disruptions or defects occur. Our contribution is the proposal of a diagnostic method when multiple faults type actuators or sensors affect the system.