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

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Wu ◽  
Xiaojuan Chen ◽  
Zhaohua Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Finite Impulse Response ◽  
Feedforward Neural Network ◽  
System Stability ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Hidden Layer

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.

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.

Predicting Surface Roughness in Turning Operation Using Extreme Learning Machine

2014 ◽  
Vol 554 ◽  
pp. 431-435 ◽  
Author(s):  
Ahmad Nooraziah ◽  
V. Janahiraman Tiagrajah
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Extreme Learning Machine ◽  
Cutting Parameters ◽  
Feedforward Neural Network ◽  
Support Vector ◽  
Satisfactory Solution ◽  
Modeling Techniques ◽  
Learning Machine ◽  
Hidden Layer

Prediction model allows the machinist to determine the values of the cutting performance before machining. According to literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Recently, Extreme Learning Machine (ELM) has been introduced as the alternative to overcome the limitation from the previous methods. ELM has similar structure as single hidden layer feedforward neural network with analytically to determine output weight. By comparing to Response Surface Methodology, Support Vector Machine and Neural Network, this paper proposed the prediction of surface roughness using ELM method. The result indicates that ELM can yield satisfactory solution for predicting surface roughness in term of training speed and parameter selection.

scholarly journals Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine

2014 ◽  
Vol 129 ◽  
pp. 428-436 ◽  
Author(s):  
Tiago Matias ◽  
Francisco Souza ◽  
Rui Araújo ◽  
Carlos Henggeler Antunes
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Feedforward Neural Network ◽  
Learning Machine ◽  
Hidden Layer

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 One-Class Classification with Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Qian Leng ◽  
Honggang Qi ◽  
Jun Miao ◽  
Wentao Zhu ◽  
Guiping Su
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Classification Problem ◽  
Traditional Learning ◽  
Learning Speed ◽  
One Class Classifier ◽  
Learning Machine ◽  
Hidden Layer ◽  
One Class Classification ◽  
Network Approaches

One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM). The essence of ELM is that the hidden layer need not be tuned and the output weights can be analytically determined, which leads to much faster learning speed. The experimental evaluation conducted on several real-world benchmarks shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one-class classification methods.

scholarly journals Study on a Fault Identification Method of the Hydraulic Pump Based on a Combination of Voiceprint Characteristics and Extreme Learning Machine

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 894 ◽  
Author(s):  
Wanlu Jiang ◽  
Zhenbao Li ◽  
Jingjing Li ◽  
Yong Zhu ◽  
Peiyao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Recognition Accuracy ◽  
Test Sample ◽  
Training Sample ◽  
Accuracy Rate ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Axial Piston ◽  
Sample Set

Aiming at addressing the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an axial piston pump fault diagnosis method that is based on the combination of Mel-frequency cepstrum coefficients (MFCC) and the extreme learning machine (ELM) is proposed. Firstly, a sound sensor is used to realize contactless sound signal acquisition of the axial piston pump. The wavelet packet default threshold denoises the original acquired sound signals. Afterwards, windowing and framing are added to the de-noised sound signals. The MFCC voiceprint characteristics of the processed sound signals are extracted. The voiceprint characteristics are divided into a training sample set and test sample set. ELM models with different numbers of neurons in the hidden layers are established for training and testing. The relationship between the number of neurons in the hidden layer and the recognition accuracy rate is obtained. The ELM model with the optimal number of hidden layer neurons is established and trained with the training sample set. The trained ELM model is applied to the test sample set for fault diagnosis. The fault diagnosis results are obtained. The fault diagnosis results of the ELM model are compared with those of the back propagation (BP) neural network and the support vector machine. The results show that the fault diagnosis method that is proposed in this paper has a higher recognition accuracy rate, shorter training and diagnosis times, and better application prospect.

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.

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