Fault Diagnosis Based on Weighted Extreme Learning Machine With Wavelet Packet Decomposition and KPCA

2018 ◽  
Vol 18 (20) ◽  
pp. 8472-8483 ◽  
Author(s):  
Qin Hu ◽  
Aisong Qin ◽  
Qinghua Zhang ◽  
Jun He ◽  
Guoxi Sun
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Weighted Extreme Learning Machine ◽  
Learning Machine

Research of Fault Diagnosis Method Based on Improved Extreme Learning Machine

2015 ◽  
Vol 727-728 ◽  
pp. 872-875
Author(s):  
Wen Bo Na ◽  
Qing Feng Jiang ◽  
Zhi Wei Su
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Rbf Neural Networks ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Better Than

In order to improve the accuracy of diagnosis pumping, and accelerate the speed of diagnosis, a fault diagnosis model based on improved extreme learning machine (RWELM) was proposed. Firstly, it extracted the energy characteristic eigenvector of dynamometer cards of an oilfield in northern Shanxi by using wavelet packet decomposition method. Then through simulation of fault diagnosis, and compare with the extreme learning machine (ELM), RBF neural networks and support vector machine (SVM). The experimental results show that the accuracy and the speed of fault diagnosis based on the RWELM are better than the ELM, RBF neural network and SVM.

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%.

scholarly journals Improving annual precipitation prediction using extreme learning machine with wavelet packet decomposition

2021 ◽  
Author(s):  
Hua Wang ◽  
Wenchuan Wang ◽  
Yu-jin Du ◽  
Dong-mei Xu ◽  
Yi-duo Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Wavelet Packet ◽  
Moving Average ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Annual Precipitation ◽  
Wavelet Packet Decomposition ◽  
Precipitation Forecasting ◽  
Precipitation Prediction ◽  
Learning Machine

Abstract Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action as well as activity. In this paper, a novel merged precipitation prediction framework (ELM-WPD) is proposed on the Extreme learning machine (ELM) with wavelet packet decomposition (WPD). The model can be described as the following: (a) we use the WPD to decompose the original precipitation data into several sub-layers; (b) ELM model is employed to complete the forecasting calculation for the decomposed series; (c) the results are integrated to complete the final prediction. Four quantitative indexes (RMSE, MAE, R and NSEC) are employed for the comparison criteria. The results are compared with Back-propagation neural network (BPNN), autoregressive integrated moving average model (ARIMA), ELM, BPNN-WPD model, ARIMA-WPD, indicating that the ELM-WPD model has better performance than other models used in this paper. Hence, the proposed method can provide higher accuracy and reliability for annual precipitation forecasting and can be extended to similar situations for precipitation forecasting.

scholarly journals Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401775144 ◽  
Author(s):  
Jun Ma ◽  
Jiande Wu ◽  
Xiaodong Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Wavelet Packet ◽  
Support Vector ◽  
Machine Method ◽  
Energy Entropy ◽  
Diagnosis Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Aiming at connatural limitations of extreme learning machine in practice, a new fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine is proposed. On one hand, the presented method can extract the more efficient features using the wavelet packet-energy entropy method, and on the other hand, the sample fuzzy membership degree matrix U, weight matrix W which is used to describe the sample imbalance, and the kernel function are introduced to construct the fuzzy kernel extreme learning machine model with high accuracy and reliability. The experimental results of rolling bearing and check valve are obtained and analyzed in MATLAB 2010b. The results show that the proposed fuzzy kernel extreme learning machine method can obtain fairly or slightly better classification performance than the traditional extreme learning machine, kernel extreme learning machine, back propagation, support vector machine, and fuzzy support vector machine.

scholarly journals A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2599
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Yu Sun ◽  
Shuqing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Recognition Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Hydraulic Pump ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

scholarly journals An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ji-Yong An ◽  
Fan-Rong Meng ◽  
Zi-Ji Yan
Keyword(s):  
Ion Channel ◽  
Extreme Learning Machine ◽  
Nuclear Receptor ◽  
Drug Target ◽  
Computational Method ◽  
Evolutionary Information ◽  
Support Vector ◽  
Weighted Extreme Learning Machine ◽  
Speed Up ◽  
Learning Machine

Abstract Background Prediction of novel Drug–Target interactions (DTIs) plays an important role in discovering new drug candidates and finding new proteins to target. In consideration of the time-consuming and expensive of experimental methods. Therefore, it is a challenging task that how to develop efficient computational approaches for the accurate predicting potential associations between drug and target. Results In the paper, we proposed a novel computational method called WELM-SURF based on drug fingerprints and protein evolutionary information for identifying DTIs. More specifically, for exploiting protein sequence feature, Position Specific Scoring Matrix (PSSM) is applied to capturing protein evolutionary information and Speed up robot features (SURF) is employed to extract sequence key feature from PSSM. For drug fingerprints, the chemical structure of molecular substructure fingerprints was used to represent drug as feature vector. Take account of the advantage that the Weighted Extreme Learning Machine (WELM) has short training time, good generalization ability, and most importantly ability to efficiently execute classification by optimizing the loss function of weight matrix. Therefore, the WELM classifier is used to carry out classification based on extracted features for predicting DTIs. The performance of the WELM-SURF model was evaluated by experimental validations on enzyme, ion channel, GPCRs and nuclear receptor datasets by using fivefold cross-validation test. The WELM-SURF obtained average accuracies of 93.54, 90.58, 85.43 and 77.45% on enzyme, ion channels, GPCRs and nuclear receptor dataset respectively. We also compared our performance with the Extreme Learning Machine (ELM), the state-of-the-art Support Vector Machine (SVM) on enzyme and ion channels dataset and other exiting methods on four datasets. By comparing with experimental results, the performance of WELM-SURF is significantly better than that of ELM, SVM and other previous methods in the domain. Conclusion The results demonstrated that the proposed WELM-SURF model is competent for predicting DTIs with high accuracy and robustness. It is anticipated that the WELM-SURF method is a useful computational tool to facilitate widely bioinformatics studies related to DTIs prediction.

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