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.

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.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM

2010 ◽  
Vol 121-122 ◽  
pp. 813-818 ◽  
Author(s):  
Wei Guo Zhao ◽  
Li Ying Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Intelligent Fault Diagnosis ◽  
Fuzzy Support Vector Machine ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

On the basis of wavelet packet-characteristic entropy(WP-CE) and multiclass fuzzy support vector machine(MFSVM), the author proposes a new fault diagnosis method of vibrating of hearings，in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted，the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample multiclass fuzzy support vector machine is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.

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 A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Mingliang Liang ◽  
Dongmin Su ◽  
Daidi Hu ◽  
Mingtao Ge
Keyword(s):  
Extreme Learning Machine ◽  
Rolling Bearing ◽  
Local Characteristic ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Evaluation Approach ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Intrinsic Scale

A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and relative entropy of intrinsic scale components were calculated. According to the distance-based evaluation approach, sensitivity features can be extracted. Finally, sensitivity features were input to extreme learning machine to identify rolling bearing fault types. Experimental results show that the proposed method achieved better performance than support vector machine (SVM) and backpropagation (BP) neural network methods.

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