A Transformer Fault Diagnosis Method Based on Parameters Optimization of Hybrid Kernel Extreme 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.

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

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096511666180611102108 ◽

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.

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Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine

Complexity ◽

10.1155/2019/4031795 ◽

2019 ◽

Vol 2019 ◽

pp. 1-19 ◽

Cited By ~ 19

Author(s):

Zhijian Wang ◽

Likang Zheng ◽

Junyuan Wang ◽

Wenhua Du

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Local Optimum ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Krill Herd Algorithm ◽

Krill Herd ◽

Diagnosis Method ◽

Kernel Extreme Learning Machine ◽

Learning Machine

In this paper, a novel bearing intelligent fault diagnosis method based on a novel krill herd algorithm (NKH) and kernel extreme learning machine (KELM) is proposed. Firstly, multiscale dispersion entropy (MDE) is used to extract fault features of bearings to obtain a set of fault feature vectors composed of dispersion entropy. Then, it is imported into the kernel extreme learning machine for fault diagnosis. But considering the kernel function parameters σ and the error penalty factor C will affect the classification accuracy of the kernel extreme learning machine, this paper uses the novel krill herd algorithm (NKH) for their optimization. The opposite populations are added to the NKH in the initialization of population to improve its speed and prevent local optimum, and during the period of looking for the optimal solution, the impulse operator is introduced to ensure it has enough impulse to rush out of the local optimal once into the local optimum. Finally, in order to verify the effectiveness of the proposed method, it was applied to the bearing fault experiment of Case Western Reserve University and XJTU-SY bearing data set. The results show that the proposed method not only has good fault diagnosis performance and generalization but also has fast convergence speed and does not easily fall into the local optimum. Therefore, this paper provides a method for fault diagnosis under different loads. Meanwhile, the new method (NKH-KELM) is compared and analyzed with other mainstream intelligent bearing fault diagnosis methods to verify the effectiveness and accuracy of the proposed method.

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A Rolling Bearing Fault Diagnosis Method Based on Variational Mode Decomposition and an Improved Kernel Extreme Learning Machine

Applied Sciences ◽

10.3390/app7101004 ◽

2017 ◽

Vol 7 (10) ◽

pp. 1004 ◽

Cited By ~ 28

Author(s):

Ke Li ◽

Lei Su ◽

Jingjing Wu ◽

Huaqing Wang ◽

Peng Chen

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Rolling Bearing ◽

Variational Mode Decomposition ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Mode Decomposition ◽

Diagnosis Method ◽

Kernel Extreme Learning Machine ◽

Learning Machine

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

Advances in Mechanical Engineering ◽

10.1177/1687814017751446 ◽

2018 ◽

Vol 10 (1) ◽

pp. 168781401775144 ◽

Cited By ~ 6

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.

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