Notice of Retraction: Study of a Fault Diagnosis Method Based on Elman Neural Network and Trouble Dictionary

2008 ◽  
Author(s):  
Qu Dong-Cai ◽  
Feng Yu-Guang ◽  
Fan Shao-Li ◽  
Chen Qi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Elman Neural Network ◽  
Diagnosis Method

Fault diagnosis method of peak-load-regulation steam turbine based on improved PCA-HKNN artificial neural network

2021 ◽  
pp. 1748006X2110105
Author(s):  
Yifan Wu ◽  
Wei Li ◽  
Deren Sheng ◽  
Jianhong Chen ◽  
Zitao Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Power Plants ◽  
Peak Load ◽  
Clean Energy ◽  
Steam Turbines ◽  
Peak Shaving ◽  
Diagnosis Method ◽  
Load Regulation

Clean energy is now developing rapidly, especially in the United States, China, the Britain and the European Union. To ensure the stability of power production and consumption, and to give higher priority to clean energy, it is essential for large power plants to implement peak shaving operation, which means that even the 1000 MW steam turbines in large plants will undertake peak shaving tasks for a long period of time. However, with the peak load regulation, the steam turbines operating in low capacity may be much more likely to cause faults. In this paper, aiming at peak load shaving, a fault diagnosis method of steam turbine vibration has been presented. The major models, namely hierarchy-KNN model on the basis of improved principal component analysis (Improved PCA-HKNN) has been discussed in detail. Additionally, a new fault diagnosis method has been proposed. By applying the PCA improved by information entropy, the vibration and thermal original data are decomposed and classified into a finite number of characteristic parameters and factor matrices. For the peak shaving power plants, the peak load shaving state involving their methods of operation and results of vibration would be elaborated further. Combined with the data and the operation state, the HKNN model is established to carry out the fault diagnosis. Finally, the efficiency and reliability of the improved PCA-HKNN model is discussed. It’s indicated that compared with the traditional method, especially handling the large data, this model enhances the convergence speed and the anti-interference ability of the neural network, reduces the training time and diagnosis time by more than 50%, improving the reliability of the diagnosis from 76% to 97%.

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

Fault diagnosis method for scintillation detector based on BP neural network

2021 ◽  
Vol 16 (07) ◽  
pp. T07006
Author(s):  
Y.X. Xie ◽  
Y.J. Yan ◽  
X. Li ◽  
T.S. Ding ◽  
C. Ma
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Scintillation Detector ◽  
Diagnosis Method

Research on the Fault Diagnosis Technology of Diesel Engine Based on the Instantaneous Speed

2009 ◽  
Vol 413-414 ◽  
pp. 547-552 ◽  
Author(s):  
Yi Hu ◽  
Rui Ping Zhou ◽  
Jian Guo Yang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Vibration Amplitude ◽  
Good Effect ◽  
Cylinder Pressure ◽  
Angular Vibration ◽  
Diagnosis Method ◽  
Som Neural Network ◽  
Instantaneous Speed

The instantaneous speed signals of diesel contain lots of information about machine states, which is useful for fault diagnosis of diesel engine. Mixed fault diagnosis method of diesel engine based on the instantaneous speed has been proposed, which combines with the lower order angular vibration amplitude and SOM neural network to diagnose the cylinder pressure fault, then extracts three feature parameters of instantaneous speed to locate the fault cylinder. The method can detect the cylinder pressure fault accurately in diesel engine and locate the fault cylinder. The experimental confirmation shows that it has good effect on fault diagnosis of diesel engine.

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