Extreme Learning Machine Training Research Articles

Predictive combination model for CH4 separation and CO2 sequestration with CO2 injection into coal seams: VMD-STA-BiLSTM-ELM hybrid neural network modeling

In the experimental process of separating coal seam gas using the CO2 displacement method, establishing a predictive model for key variables is essential to optimize displacement parameters, increase coal seam gas recovery, and improve CO2 sequestration efficiency. Traditional modeling methods often struggle with the complex nature of industrial data and are susceptible to overfitting due to multicollinearity caused by long-term datasets. This paper presents a hybrid predictive model based on variational mode decomposition (VMD), a spatiotemporal attention mechanism (STA), a bidirectional long short-term memory network (BiLSTM), and an extreme learning machine (ELM). During the offline phase, VMD is used to decompose raw data into intrinsic mode functions (IMFs). The hidden state from the last time step of the STA-BiLSTM is then added to the original data to enrich the features for ELM training. In the online prediction phase, the outputs from the VMD-STA-BiLSTM and VMD-STA-BiLSTM-ELM models are combined using an error reciprocal method to generate the final prediction. The proposed model is validated with experimental datasets from CO2 displacement for coal seam CH4 under various conditions, as well as with N2-ECBM and CO2-ECBM engineering datasets. The results show that the hybrid model surpasses VMD-ELM, STA-BiLSTM-ELM, BiLSTM, STA-BiLSTM, ELM, TCN, and Attention-TCN models in predictive accuracy. Even in multi-step and rolling predictions, the model exhibits minimal impact from cumulative errors, maintaining accurate forecasts with strong generalization and robustness. It effectively captures feature patterns across different datasets and accurately predicts unknown data. The model shows potential for application in diverse scenarios and complex environments, offering reliable support and decision-making for the field application of CO2 displacement in coal seam CH4 separation. It is an effective and promising predictive approach to enhance coal seam gas recovery and CO2 sequestration efficiency.

Enhanced Epileptic Seizure Identification using Sparse ELM-ABO Fusion with Feature Reduction and Multi-class Classification

The interpretation of the electroencephalogram (EEG) signal is one method that can be utilized to diagnose epilepsy, which is one of the most prevalent brain illnesses. The length of an EEG signal is typically quite long, making it difficult to interpret manually. Extreme Learning Machine (ELM) is used to detection of Epilepsy and Seizure. But in ELM Storage space and training time is high. In order to reduce training time and storage space African Buffalo Optimization (ABO) algorithm is used. ABO is combined with Sparse ELM to improve the speed, accuracy of detection and reduce the storage space. First, Wavelet transform is used to extract relevant features. Due to their high dimensionality, these features are then reduced by using linear discriminant analysis (LDA). The proposed Hybrid Sparse ELM technique is successfully implemented for diagnosing epileptic seizure disease. For classification, the Sparse ELM-ABO classifier is applied to the UCI Epileptic Seizure Recognition Data Set training dataset, and the experimental findings are compared to those of the SVM, Sparse ELM, and ELM classifiers applied to the same database. The proposed model was tested in two scenarios: binary classification and multi-label classification. Seizure identification is the only factor in binary classification. Seizure and epilepsy identification are part of multi-label classification. It is observed that the proposed method obtained high accuracy in classification with less execution time along with performance evaluation of parameters such as prediction accuracy, specificity, precision, recall and F-score. Binary classification scores 96.08%, while multi-label classification achieves 90.89%.

Residual Compensation-Based Extreme Learning Machine for MIMO-NOMA Receiver

Extreme learning machine (ELM) uses a simple machine learning (ML) architecture that allows its implementation for smart IoT network operations. However, some inaccuracy lies between the predicted and the actual data during the ELM training, which can be caused due to the limitation of the modeling representation. This paper thus investigates a residual compensation-based ELM (RC-ELM) for its application in designing a receiver for MIMO-NOMA aided IoT systems. In RC-ELM, the base ELM layers determine the relationship between the transmitted and received data and the additional layers of the RC-ELM attempt to compensate the error introduced during the training mechanism. The analysis of the appropriate number of compensation layers for training error minimization is conducted on the basis of the bit error rate (BER) and the error vector magnitude (EVM) performances of the RC-ELM training. A minimum BER improvement of 5% for user 1 and 18% for user 2 is shown with with the aid of RC-ELM for a two-IoT user instance. The EVM is marginally increased by 0.0008% for user 1 and 1.61% for user 2 in training stage. Besides, the RC-ELM receiver is also compared to the minimum mean square error (MMSE), the classic ELM and the trained multilayer perceptron (MLP) receivers in terms of BER and EVM. Both of the ELM receivers show improved performances with respect to the other receivers.

A GPU-based accelerated ELM and deep-ELM training algorithms for traditional and deep neural networks classifiers

• In this paper, we have proposed novel parallel ELM and D-ELM algorithms on the GPU. The benefits of these parallel algorithms include reducing the training time of the algorithms and reducing the runtime significantly. Also, remarkably predictive 95 accuracy is presented. To the best of our knowledge, this is the first kind of parallelization of the D-ELM in which the high computing power of graphics processing equipped with CUDA is utilized to parallelize. Our main contribution is as follows. • All sub-operations of ELM and D-ELM which need to be parallelized 100 are parallelized on GPU, thus benefiting from the memory hierarchy. In the meantime, several optimizations are adopted to improve the training time and performance of our parallelization. • Maximizing parallel performance: It is very important organizing the algorithm in computational blocks that are able to run independently. 105 Also, the communication between the blocks is minimized to achieve better performance. • Improve the usage of memory hierarchy: It is especially important to execution maximize the number of operations on data stored in shared memory. This strategy is applied to ELM and D-ELM 110 algorithms. • Using of CUDA Optimal Libraries: In some operations of ELM and D-ELM algorithms, these useful tools have been used for improving the performance. • Maximizing the independent and concurrent execution of the times 115 auto-encoders in the D-ELM Algorithm: Given that many computational operations are located in this section, their concurrent and independent execution is extraordinary for improving the performance. • The implementation of the standard of ELM and D-ELM has a weak 120 learning speed by increasing the scale of data. We have optimally implemented serial versions of ELM and D-ELM algorithms as a basis and brought the cost of training time for different strategies using data with different scales. Serial part evaluations show that the cost of training time for ELM and D-ELM algorithms and the size of 125 the dataset have almost linearly related. • Our proposed parallel algorithms have well training time and prediction accuracy and offer high performance for iterative calculations. Since they have high computing power so another variant of machine learning algorithms that have high iterative 130 computations can benefit from our proposed methods. The extreme learning machine (ELM) has been effectively used for training single-layer neural networks. In recent years, great attention has been paid to deep extreme learning machine (D-ELM) structures. Deep neural network structures are trained via the ELM method. Some stacked auto-encoders followed by a simple ELM layer can be usually used for solving classification and regression tasks. Although ELM has been employed for speeding up the training process of the neural network, D-ELM based models suffer from some issues such as the time complexity and running time. In this paper, we explore how the evaluation of ELM and D-ELM can be accelerated. GPUs are used to speed up the training process of ELM and D-ELM models. In the proposed method, three separate phases are considered for the algorithms. In the first phase, loading and pre-processing the data are performed serially in the CPU. In the second and third phases, which respectively are the training and testing phases of the algorithm, all the matrix operations of the algorithms are implemented in parallel mode using the GPU memory hierarchy. Also, having access to highly efficient computational libraries, additional support is provided for GPU-based parallel computing. In the simulation setup, five sets of the database are applied to train the ELM and D-ELM on both CPU and GPU platforms. The results obtained show the proposed approach based on GPUs can remarkable save running time. Although both serial and parallel methods measure approximately the same accuracy, the parallel methods provided for the models reduce the run time significantly.

SAVE‐AS: Accelerating convergence in network traffic prediction through adaptive optimized variational mode decomposition and an integrated extreme learning machine

SummaryNonlinearity and nonstationary data affect the prediction accuracy of network traffic. A practical solution is to use an integrated modeling method based on time‐series decomposition and an extreme learning machine (ELM). The original network traffic data series are decomposed in this paper using variational mode decomposition (VMD), and then each subdata series is subjected to phase space reconstruction (PSR). Finally, ELM trains a model for predicting network traffic. We used scalable artificial bee colony (SABC) algorithms to optimize parameter selection throughout the modeling process. However, several processes, such as VMD decomposition, PSR, and ELM training, will be repeated during iterative model optimization, resulting in a significant increase in computational complexity. This article proposes an adaptive selection mechanism for decomposing time series, which named is SABC‐VMD‐ELM prediction method with an adaptive selection (SAVE‐AS) method. From gray correlation degree and approximate entropy, an adaptive selection operator is constructed that can adaptively select the number of data sets after VMD optimization decomposition. The ELM model is trained using several subdata sequences that satisfy the conditions rather than all subdata sequences to minimize computational complexity in modeling. Two publicly available data sets of Mackey–Glass and Lorenz chaotic time series were used; in addition, actual network traffic data from the MAWI Working Group's WIDE Backbone was simulated. It is demonstrated that the prediction model can reduce computational complexity and accelerate convergence speed while maintaining the model's predictive accuracy. In three data sets, training time was reduced by 25.25%, 23.87%, and 41.36%, respectively.

Robust penalized extreme learning machine regression with applications in wind speed forecasting

In extreme learning machine (ELM) framework, the hidden layer setting determines its generalization ability; and in presence of outliers in the training set, weights between hidden layer and output layer based on the least squares would be overly estimated. To address these two problems in ELM implementation, we extend robust penalized statistical framework in ELM and propose a general robust penalized ELM, which consists of two components (robust loss function and regularization item), for regression to improve the efficiency of ELM training with more elegant neural network structure resulting in more accurate predictions. We investigate six different loss functions ( $$l_1$$ -norm loss, $$l_2$$ -norm loss, Huber loss, Bisquare loss, exponential squared loss and Lncosh loss) and two regularization strategies (lasso penalty and ridge penalty). Furthermore, we present two training procedures for our robust penalized ELM via iterative reweighted least squares method with hyper-parameter setting by cross-validation with lasso penalty and ridge penalty, respectively. Finally, the proposed robust penalized ELM is employed in an ultra-short-term wind speed forecasting study, and our framework is confirmed in this specific application producing more effective predictions according to the multi-step forecasting performance.

A hybrid robust system considering outliers for electric load series forecasting

Electric load forecasting has become crucial to the safe operation of power grids and cost reduction in the production of power. Although numerous electric load forecasting models have been proposed, most of them are still limited by poor effectiveness in the model training and a sensitivity to outliers. The limitations of current methods may lead to extra operational costs of a power system or even disrupt its power distribution and network safety. To this end, we propose a new hybrid load-forecasting model, which is based on a robust extreme-learning machine and an improved whale optimization algorithm. Specifically, Huber loss, which is insensitive to outliers, is proposed as the objective function in extreme learning machine (ELM) training. In addition, an improved whale optimization algorithm is designed for the robust ELM training, in which a cellular automaton mechanism is used to enhance the local search. To verify our improved whale optimization algorithm, some experiments were then conducted based on seven benchmark test functions. Due to the enhancement of the local search, the improved optimizer was around 7% superior to the basic. Finally, our proposed hybrid forecasting model was validated by two real electric load datasets (Nanjing and New South Wales), and the experimental results confirmed that the proposed hybrid load-forecasting model could achieve satisfying improvements in both datasets.

Optimization of diesel engine dual-variable geometry turbocharger regulated two-stage turbocharging system based on radial basis function neural network-quantum genetic algorithm

ABSTRACT Aiming at the problem of power and economy reduction in high-altitude operation of diesel engines, a radial basis function neural network-quantum genetic algorithm (RBFNN-QGA) optimization method is proposed to optimize diesel engine dual-variable geometry turbocharger (VGT) regulated two-stage turbocharging (RTST) system. Firstly, the GT-power simulation model of dual-VGT RTST diesel engine was established, and then calibrated with test data. The latin hypercube sampling (LHS) algorithm was selected for the experimental design, and the back propagation neural network (BPNN), RBFNN, and extreme learning machine (ELM) were used to establish three diesel engine high-altitude performance prediction models based on the sample data, to compare the ability of the three neural networks in predicting the performance of diesel engines. Finally, RBFNN is coupled with QGA to establish the optimization model of dual-VGT RTST system at different altitudes with torque, fuel consumption rate as the optimization target, and vortex front row temperature and in-cylinder combustion pressure as the constraints. The study results show that three kinds of neural networks have achieved satisfactory prediction results, BPNN and RBFNN are better than ELM in prediction accuracy, and ELM training is faster than BPNN and RBFNN; QGA converges faster and has higher optimization efficiency than GA, and the maximum torque of the optimized dual-VGT diesel engine is restored to more than 85% of the plain level, compared with the single-turbocharged diesel engine and the single-VGT two-stage turbocharged diesel engine, the low-speed torque of the dual-VGT RTST diesel engine is increased by 384% and 100%. This optimization model has better optimization effect and efficiency.

Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network.

Insulated Gate Bipolar Transistor (IGBT) is a high-power switch in the field of power electronics. Its reliability is closely related to system stability. Once failure occurs, it may cause irreparable loss. Therefore, potential fault diagnosis methods of IGBT devices are studied in this paper, and their classification accuracy is tested. Due to the shortcomings of incomplete data application in the traditional method of characterizing the device state based on point frequency parameters, a fault diagnosis method based on full frequency threshold screening was proposed in this paper, and its classification accuracy was detected by the Extreme Learning Machine (ELM) algorithm. The randomly generated input layer weight ω and hidden layer deviation lead to the change of output layer weight β and then affect the overall output result. In view of the errors and instability caused by this randomness, an improved Finite Impulse Response Filter ELM (FIR-ELM) training algorithm is proposed. The filtering technique is used to initialize the input weights of the Single Hidden Layer Feedforward Neural Network (SLFN). The hidden layer of SLFN is used as the preprocessor to achieve the minimum output error. To reduce the structural risk and empirical risk of SLFN, the simulation results of 300 groups of spectral data show that the improved FIR-ELM algorithm significantly improves the training accuracy and has good robustness compared with the traditional extreme learning machine algorithm.

Indoor Geomagnetic Positioning Using the Enhanced Genetic Algorithm-Based Extreme Learning Machine

Indoor positioning using the geomagnetic field has become a popular technique because of the infrastructure-free characteristic and the ubiquitous magnetic signals in indoor environments. Geomagnetic intensity will generate fluctuations due to the factors such as device heterogeneity, dates, and electronic facilities. Stable geomagnetic data are essential for high-precision positioning. To find a robust and low time-consuming geomagnetic positioning model (GPM), the extreme learning machine (ELM) optimized by the enhanced genetic algorithm (EGA) is constructed. The EGA with three optimization strategies is designed to find the best initial parameters solutions for the ELM. Magnetic patterns collected by different participants using different mobile phones on different dates are extracted characteristics and divided into segments for ELM training. Extensive experiments are conducted to evaluate the proposed model’s performance. The experimental results demonstrate that the EGA-based ELM model can achieve meter-level location accuracy and has good robustness under different testing conditions. The model construction is much faster than that of the popular learning algorithms, such as the convolutional neural networks (CNNs) and backpropagation (BP) networks.

Intelligent ECMS for Connected Plug-in Hybrid Electric Vehicles

Abstract Equivalent consumption minimum strategy (ECMS) is promising for real-time implementation in plug-in hybrid electric vehicles (PHEVs). However, in order to achieve near-optimal fuel economy, it is necessary to dynamically tune the equivalent factor (EF) which is a challenging issue. This study proposes a traffic information-enabled EF adaptation scheme for ECMS by using a machine learning method, namely, extreme learning machine (ELM). First, the relationship between the EF and the co-state value of the Pontryagin’s minimum principle (PMP) method is established. Second, an ELM is built with 12 input variables that are related with vehicle states and traffic information. Third, real-world emulated speed files are generated in IPG CarMaker system, and then used for ELM training and testing. Finally, simulations performed in the CarMaker system demonstrate that the proposed strategy processes satisfactory fuel economy compared to the global optimization method PMP.

Building change detection from multi-source remote sensing images based on multi-feature fusion and extreme learning machine

ABSTRACT In this paper, in order to improve the accuracy of multi-source remote sensing image building change detection, we propose a method based on multi-feature fusion (MFF) and extreme learning machine (ELM) training. Firstly, we fuse the spectral feature difference image (DI) and textural feature (grey level co-occurrence matrix) DI obtained by change vector analysis (CVA), morphological building index DI, and shape feature DI obtained by subtraction to construct the final DI. Secondly, the coarse change detection map obtained by selecting a threshold for the DI saliency map obtained by the use of the frequency-domain significance (FDS) method is pre-classified by the fuzzy c-means (FCM) clustering algorithm. Finally, the neighborhood features obtained from the original images and the feature images of the changed pixels (buildings) and the unchanged pixels in the coarse change map are extracted and used as reliable samples for the ELM training. By using the trained ELM classifier, undetermined pixels are further separated into changed and unchanged classes. Finally, we combine the ELM classification result and the preclassification result together to form the final building change map. Experiments on two real multi-source datasets show that the proposed method can result in a significant improvement in multi-source remote sensing image building change detection performance.

An Optimized Parallel Implementation of Non-Iteratively Trained Recurrent Neural Networks

Abstract Recurrent neural networks (RNN) have been successfully applied to various sequential decision-making tasks, natural language processing applications, and time-series predictions. Such networks are usually trained through back-propagation through time (BPTT) which is prohibitively expensive, especially when the length of the time dependencies and the number of hidden neurons increase. To reduce the training time, extreme learning machines (ELMs) have been recently applied to RNN training, reaching a 99% speedup on some applications. Due to its non-iterative nature, ELM training, when parallelized, has the potential to reach higher speedups than BPTT. In this work, we present Opt-PR-ELM, an optimized parallel RNN training algorithm based on ELM that takes advantage of the GPU shared memory and of parallel QR factorization algorithms to efficiently reach optimal solutions. The theoretical analysis of the proposed algorithm is presented on six RNN architectures, including LSTM and GRU, and its performance is empirically tested on ten time-series prediction applications. Opt-PR-ELM is shown to reach up to 461 times speedup over its sequential counterpart and to require up to 20x less time to train than parallel BPTT. Such high speedups over new generation CPUs are extremely crucial in real-time applications and IoT environments.

Accelerating ELM training over data streams

In the field of machine learning, offline training and online training occupy the same important position because they coexist in many real applications. The extreme learning machine (ELM) has the characteristics of fast learning speed and high accuracy for offline training, and online sequential ELM (OS-ELM) is a variant of ELM that supports online training. With the explosive growth of data volume, running these algorithms on distributed computing platforms is an unstoppable trend, but there is currently no efficient distributed framework to support both ELM and OS-ELM. Apache Flink is an open-source stream-based distributed platform for both offline processing and online data processing with good scalability, high throughput, and fault-tolerant ability, so it can be used to accelerate both ELM and OS-ELM. In this paper, we first research the characteristics of ELM, OS-ELM and distributed computing platforms, then propose an efficient stream-based distributed framework for both ELM and OS-ELM, named ELM-SDF, which is implemented on Flink. We then evaluate the algorithms in this framework with synthetic data on distributed cluster. In summary, the advantages of the proposed framework are highlighted as follows. (1) The training speed of FLELM is always faster than ELM on Hadoop and Spark, and its scalability behaves better as well. (2) Response time and throughput of FLOS-ELM achieve better performance than OS-ELM on Hadoop and Spark when the incremental training samples arrive. (3) The response time and throughput of FLOS-ELM behave better in native-stream processing mode when the incremental data samples are continuously arriving.

Facial micro-expression recognition: A machine learning approach

Micro-expression recognition is a growing research area owing to its application in revealing subtle intention of humans, especially while under high stake conditions. With the rapid increase in security issues all over the world, the use of micro-expressions to understand one's state of mind has received major interest. Micro-expressions are characterized by short duration and low intensity, hence, efforts to train humans in recognizing them have resulted in very low performances. Automatic recognition of micro-expressions using machine learning techniques thus promises a more effective result and saves time and resources. In this study, we explore the use of Extreme Learning Machine (ELM) for micro-expression recognition because of its fast learning ability and higher performance when compared with other models. Support Vector Machine (SVM) is used as a baseline model and its recognition performance and its training time compared with ELM training time. Feature extraction is performed on apex micro-expression frames using Local Binary Pattern (LBP) and on micro-expression videos divided into image sequences using a spatiotemporal feature extraction technique called Local Binary Pattern on Three Orthogonal Planes (LBP-TOP). Evaluation of the two models is performed on spontaneous facial micro-expression samples acquired from Chinese Academy of Sciences (CASME II). Results obtained from the experiments show that ELM produces a higher recognition performance than SVM in terms of accuracy, precision, recall and F-score when temporal features are used. Comparison between SVM and ELM training time also shows that ELM learns faster than SVM. An average training time of 0.3405 seconds is achieved for SVM while an average training time of 0.0409 seconds is achieved for ELM for the five selected micro-expression classes. This study shows that automatic recognition of micro-expressions is produces a better result when temporal features and a machine learning algorithm with fast learning speed are used.

Extreme-Learning-Machine-Based Noniterative and Iterative Nonlinearity Mitigation for LED Communication Systems

This work concerns receiver design for light emitting diode (LED) communications where the LED nonlinearity can severely degrade the performance of communications. We propose extreme learning machine (ELM) based non-iterative receivers and iterative receivers to effectively handle the LED nonlinearity and memory effects. For the iterative receiver design, we also develop a data-aided receiver, where data is used as virtual training sequence in ELM training. It is shown that the ELM based receivers significantly outperform conventional polynomial based receivers; iterative receivers can achieve huge performance gain compared to non-iterative receivers; and the data-aided receiver can reduce training overhead considerably. This work can also be extended to radio frequency communications, e.g., to deal with the nonlinearity of power amplifiers.

Convolutional neural networks and extreme learning machines for malware classification

Research in the field of malware classification often relies on machine learning models that are trained on high-level features, such as opcodes, function calls, and control flow graphs. Extracting such features is costly, since disassembly or code execution is generally required. In this paper, we conduct experiments to train and evaluate machine learning models for malware classification, based on features that can be obtained without disassembly or code execution. Specifically, we visualize malware samples as images and employ image analysis techniques using both two-dimensional images and one-dimensional vectors derived from images. We consider two machine learning techniques, namely, convolutional neural networks (CNN) and extreme learning machines (ELM). For images we find that ELMs can achieve accuracies on par with CNNs, yet ELM training requires less than 2% of the time needed to train a comparable CNN. We also find that ELMs and CNNs perform as well when trained on one-dimensional data as when trained on two-dimensional data. In this latter case, ELMs are faster to train than CNNs, but only by a relatively small factor as compared to image-based training.

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

The capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. The classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. This current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. The results are evaluated based on several statistical measures and graphical presentations. The river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

A personalized diagnosis method to detect faults in gears using numerical simulation and extreme learning machine

Fault classification methods are a long-term research focus in both science society and engineering application. Generally, every real-world running mechanical system is exhibit personalized vibration behaviors and the corresponding fault samples of such systems are difficult to be obtained. Therefore, extreme learning machine (ELM), a typical fault classification method is failed to attain agreeable fault detection results. In this paper, a personalized fault diagnosis method using finite element method (FEM) simulation and ELM is proposed to detect faults in gears. The method includes three steps. Firstly, The FEM model of gears with faults is constructed to obtain fault samples (simulation signals). Secondly, to achieve ELM training process, the meshing frequency components of each simulation signal is separated into sub-signals and the corresponding time and time–frequency domains indicators are served as training samples. Finally, the measured vibration signals of gear transmission systems are employed as testing samples of trained ELM to recognize its fault types. The classification accuracy ratios of gear states in a cracked teeth of driving gear, a peeled teeth of driving gear, a broken teeth of driving gear, a peeled teeth of driving gear and a broken teeth of driven gear, a broken teeth of driving gear and a broken teeth of driven gear are 85%, 90%, 92.5%, 90% and 85% respectively. It is expect that the proposed personalized fault diagnosis method can set up a bridge between fault classification methods and real-world applications.

Temperature extraction for Brillouin optical fiber sensing system based on extreme learning machine

The use of extreme learning machine (ELM) network to extract temperature distribution from the measured Brillouin gain spectra (BGSs) along the sensing fiber obtained by Brillouin optical fiber sensors is proposed and demonstrated experimentally. Compared with conventional curve fitting method (CFM), ELM network trained by a set of ideal BGSs can extract temperature information directly from the measured BGSs obtained by Brillouin optical time domain reflectometer (BOTDR) system without the need of determining Brillouin frequency shift (BFS) and converting BFS to temperature. The BGSs linewidth is taken into account to construct the ideal BGSs by using Pseudo-Voigt curve for ELM training. The performance of ELM is analyzed in detail and compared with that of widely-used Lorentzian CFM, and the experiment results show that ELM can provide higher accuracy even at large frequency scanning step and faster processing speed. Therefore, the proposed ELM approach is feasible and effective for temperature extraction in Brillouin optical fiber sensing system.