scholarly journals Imbalanced Data Fault Diagnosis Based on an Evolutionary Online Sequential Extreme Learning Machine

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1204 ◽  
Author(s):  
Wei Hao ◽  
Feng Liu
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Imbalanced Data ◽  
Classification Performance ◽  
Complex Data ◽  
Minority Class ◽  
Diagnosis Model ◽  
Learning Machine ◽  
Hidden Layer

To quickly and effectively identify an axle box bearing fault of high-speed electric multiple units (EMUs), an evolutionary online sequential extreme learning machine (OS-ELM) fault diagnosis method for imbalanced data was proposed. In this scheme, the resampling scale is first determined according to the resampling empirical formulation, the K-means synthetic minority oversampling technique (SMOTE) method is then used for oversampling the minority class samples, a method based on Euclidean distance is applied for undersampling the majority class samples, and the complex data features are extracted from the reconstructed dataset. Second, the reconstructed dataset is input into the diagnosis model. Finally, the artificial bee colony (ABC) algorithm is used to globally optimize the combination of input weights, hidden layer bias, and the number of hidden layer nodes for an OS-ELM, and the diagnosis model is allowed to evolve. The proposed method was tested on the axle box bearing monitoring data of high-speed EMUs, on which the position of the axle box bearings was symmetrical. Numerical testing proved that the method has the characteristics of faster detection and higher classification performance regarding the minority class data compared to other standard and classical algorithms.

Two-step fault diagnosis framework for rolling element bearings with imbalanced data based on GSA-WELM and GSA-ELM

2017 ◽  
Vol 232 (16) ◽  
pp. 2937-2947 ◽  
Author(s):  
Yuan Lan ◽  
Xiaohong Han ◽  
Weiwei Zong ◽  
Xiaojian Ding ◽  
Xiaoyan Xiong ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Gravitational Search Algorithm ◽  
Imbalanced Data ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Misclassification Cost ◽  
Weighted Extreme Learning Machine ◽  
Rolling Element ◽  
Learning Machine

Rolling element bearings constitute the key parts on rotating machinery, and their fault diagnosis is of great importance. In many real bearing fault diagnosis applications, the number of fault data is much less than the number of normal data, i.e. the data are imbalanced. Many traditional diagnosis methods will get low accuracy because they have a natural tendency to favor the majority class by assuming balanced class distribution or equal misclassification cost. To deal with imbalanced data, in this article, a novel two-step fault diagnosis framework is proposed to diagnose the status of rolling element bearings. Our proposed framework consists of two steps for fault diagnosis, where Step 1 makes use of weighted extreme learning machine in an effort to classify the normal or abnormal categories, and Step 2 further diagnoses the underlying anomaly in detail by using preliminary extreme learning machine. In addition, gravitational search algorithm is applied to further extract the significant features and determine the optimal parameters of the weighted extreme learning machine and extreme learning machine classifiers. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our Institute, and the empirical results show that our approach is really fast and can achieve the diagnosis accuracies more than 96%.

Research on Transformer Fault Diagnosis Based on Online Sequential Extreme Learning Machine

2019 ◽  
Vol 12 (5) ◽  
pp. 408-413
Author(s):  
Yuancheng Li ◽  
Xiaohan Wang ◽  
Yingying Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Experimental Result ◽  
Dissolved Gas ◽  
Traditional Methods ◽  
Electric System ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Transformer Fault

Background: Transformer is one of the most important pivot equipment in an electric system which undertakes major responsibility. Therefore, it is very important to identify the fault of the transformer accurately and transformer fault diagnosis technology becomes one topic with great research value. Methods: In this paper, after analyzing the shortcomings of traditional methods, we have proposed a transformer fault diagnosis method based on Online Sequential Extreme Learning Machine (OS-ELM) and dissolved gas-in-oil analysis. This method has better precision than some commonly used methods at present. Furthermore, OS-ELM is more efficient than ELM. In addition, we analyze the effect of different parameter selection on the performance of the model by contrast experiments. Results: The experimental result shows that OS-ELM has certain promotion in precision than some traditional methods and can obviously improve the speed of training than ELM. Besides, it is known that the number of neurons in the hidden layer and the size of dataset have a great effect on the model. Conclusion: The transformer fault diagnosis method based on OS-ELM can effectively identify the faults and more efficient than ELM.

Extreme Learning Machine in the Breaker Fault Diagnosis

2014 ◽  
Vol 960-961 ◽  
pp. 896-899
Author(s):  
Dan Jiang ◽  
Shu Tao Zhao ◽  
Jian Feng Ren ◽  
Yu Tao Xu
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
High Voltage ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
High Voltage Circuit Breaker ◽  
Vibration Signal Analysis ◽  
Learning Machine ◽  
Hidden Layer ◽  
New Diagnosis

In order to improve the diagnosis method of the existing high-voltage circuit breaker fault, demonstrated a new diagnosis methord of mechanical failure of high voltage circuit breaker based on vibration signal. According to the factors of high voltage circuit breaker failure and the features of Single-hidden Layer Feedforward Neural Network, SLFN, a method of high voltage circuit breaker fault diagnosis proposed based on Extreme Learning Machine (ELM). Finally, the experiment proves the effectiveness of this method for breaker fault diagnosis based on vibration signal analysis and ELM.

scholarly journals Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Gao ◽  
Jiangang Lv
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Feature Space ◽  
Single Stage ◽  
Training Data ◽  
Learning Machine ◽  
Hidden Layer ◽  
Signal Processing Methods ◽  
Hidden Nodes ◽  
Selection Of

Single-Stage Extreme Learning Machine (SS-ELM) is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM) has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.

scholarly journals An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine

2020 ◽  
Vol 10 (7) ◽  
pp. 2386
Author(s):  
Sumin Guo ◽  
Bo Wu ◽  
Jingyu Zhou ◽  
Hongyu Li ◽  
Chunjian Su ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Analog Circuits ◽  
Analog Circuit ◽  
Classification Performance ◽  
Fault Classification ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis ◽  
Learning Machine ◽  
Circle Model

The fault diagnosis of analog circuits faces problems, such as inefficient feature extraction and fault identification. To solve the problems, this paper combines the circle model and the extreme learning machine (ELM) into a fault diagnosis method for the linear analog circuit. Firstly, a circle model for the voltage features of fault elements was established in the complex domain, according to the relationship between the circuit response, element position and circuit topology. To eliminate the impacts of tolerances and signal aliasing, the 3D feature was introduced to make the indistinguishable features in fuzzy groups distinguishable. Fault feature separability is very important to improve the fault diagnosis accuracy. In addition, an effective classier can improve the precision and the time taken. With less computational complexity and a simpler process, the ELM algorithm has a fast speed and a good classification performance. The effectiveness of the proposed method is verified by simulation. The simulation results show the ELM-based algorithm classifier with the circle model can enhance precision and reduce time taken by about 80% in comparison with other methods for analog circuit fault diagnosis. To sum up, this proposed method offers a fault diagnosis method that reduces the complexity in generating fault features, improves the isolation probability of faults, speeds up fault classification, and simplifies fault testing.

scholarly journals Improve Efficiency of Extreme Learning Machine based on Chaos Particle Swarm Optimization Method

2019 ◽  
Vol 8 (03) ◽  
pp. 24491-24501
Author(s):  
Yuwen Pan Zhan Wen ◽  
Yahui Chen, Wenzao Li
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Particle Swarm ◽  
Optimization Method ◽  
Running Speed ◽  
Swarm Optimization ◽  
Fast Training ◽  
Learning Machine ◽  
Hidden Layer

Extreme Learning Machine (ELM) and Regularized Extreme Learning Machine (RELM) have advantages of fast training speed and good generalization. However, ELM/RELM often needs numerous number of hidden layer nodes to get better performance. The superabundant nodes in hidden layer maybe lead to low running speed. Thus it is not feasible to use ELM in some fields that require high speed algorithms. Therefore, in this paper, we propose an Improved ELM/RELM Optimized based on Chaos Particle Swarm Optimization (CPSO-ELM/RELM) to reduce the number of hidden layer nodes, but still maintain a desirable accuracy. At the same time, it lowers the running speed compared with other algorithms. To verify the application of this method, we design numerous experiments for ELM and RRELM. Their simulation shows that the approach improves the speed of the algorithms, and the accuracy is still high. This makes it possible to use improved CPSO-ELM/RELM in some system with high real-time requirements.

scholarly journals Exploring the Extreme Learning Machine for Classification of Brain MRIs

2019 ◽  
Vol 9 (2) ◽  
pp. 3654-3657
Keyword(s):  
Extreme Learning Machine ◽  
Principal Component ◽  
Classification Performance ◽  
Feature Reduction ◽  
Brain Diseases ◽  
Discrete Wavelet ◽  
Learning Machine ◽  
Hidden Layer ◽  
Method Accuracy

Magnetic Resonance Imaging (MRI) technique of brain is the most important aspect of diagnosis of brain diseases. The manual analysis of MR images and identifying the brain diseases is tedious and error prone task for the radiologists and physicians. In this paper 2-Dimensional Discrete Wavelet Transformation (2D DWT) is used for feature extraction and Principal Component Analysis (PCA) is used for feature reduction. The three types of brain diseases i.e. Alzheimer, Glioma and Multiple Sclerosis are considered for this work. The Two Hidden layer Extreme learning Machine (TELM) is used for classification of samples into normal or pathological. The performance of the TELM is compared with basic ELM and the simulation results indicate that TELM outperformed the basic ELM method. Accuracy, Recall, Sensitivity and F-score are considered as the classification performance measures in this paper

Multi-feature learning-based extreme learning machine for rolling bearing fault diagnosis

2021 ◽  
pp. 1748006X2110485
Author(s):  
Longkui Zheng ◽  
Yang Xiang ◽  
Chenxing Sheng
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Feature Learning ◽  
Rolling Bearing ◽  
Working Environment ◽  
Learning Methods ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Learning Machine ◽  
Hidden Layer

Rolling bearing has been becoming an important part of human life and work. The working environment of rolling bearing is very complex and variable, which makes it difficult for fault diagnosis and monitor of rolling bearing from raw vibration data. Then, in this paper, a novel multi-feature learning-based extreme learning machine is proposed for rolling bearing fault diagnosis (FL-ELM). Extreme learning machine (ELM) is a fast and generalized algorithm proposed for training single-hidden-layer feed-forward networks (SLFNs), which has fast computing speed and small testing error. The novel architecture has two hidden layers and an experience pool sandwiched between two hidden layers. The first hidden layer consists of multi-feature learning methods. The experience pool is used to sort and choose new data, with old data being filtered out. Firstly, the first hidden layer is adopted for feature extraction. Secondly, the experience pool is used to rearrange and select data, which is extracted by first hidden layer. Thirdly, ELM is employed to further learn and classify. The proposed method (FL-ELM) is applied to the rolling bearing fault diagnosis. The results confirm that the proposed method is more effective than traditional methods and standard deep learning methods.

scholarly journals Improved Butterfly Optimizer-Configured Extreme Learning Machine for Fault Diagnosis

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Helong Yu ◽  
Kang Yuan ◽  
Wenshu Li ◽  
Nannan Zhao ◽  
Weibin Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Rolling Bearing ◽  
Classification Performance ◽  
Feature Subset ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Outer Race ◽  
Rolling Ball ◽  
Learning Machine

An efficient intelligent fault diagnosis model was proposed in this paper to timely and accurately offer a dependable basis for identifying the rolling bearing condition in the actual production application. The model is mainly based on an improved butterfly optimizer algorithm- (BOA-) optimized kernel extreme learning machine (KELM) model. Firstly, the roller bearing’s vibration signals in the four states that contain normal state, outer race failure, inner race failure, and rolling ball failure are decomposed into several intrinsic mode functions (IMFs) using the complete ensemble empirical mode decomposition based on adaptive noise (CEEMDAN). Then, the amplitude energy entropies of IMFs are designated as the features of the rolling bearing. In order to eliminate redundant features, a random forest was used to receive the contributions of features to the accuracy of results, and subsets of features were set up by removing one feature in the descending order, using the classification accuracy of the SBOA-KELM model as the criterion to obtain the optimal feature subset. The salp swarm algorithm (SSA) was introduced to BOA to improve optimization ability, obtain optimal KELM parameters, and avoid the BOA deteriorating into local optimization. Finally, an optimal SBOA-KELM model was constructed for the identification of rolling bearings. In the experiment, SBOA was validated against ten other competitive optimization algorithms on 30 IEEE CEC2017 benchmark functions. The experimental results validated that the SBOA was evident over existing algorithms for most function problems. SBOA-KELM employed for diagnosing the fault diagnosis of rolling bearings obtained improved classification performance and higher stability. Therefore, the proposed SBOA-KELM model can be effectively used to diagnose faults of rolling bearings.

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