Wavelet Analysis of Fault Diagnosis Technology

2014 ◽  
Vol 651-653 ◽  
pp. 2402-2405
Author(s):  
Jia Tian
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Computer Technology ◽  
Wavelet Function ◽  
Network Technology ◽  
Knowledge Based ◽  
Diagnosis Method ◽  
Neural Network Technology

In recent years, the development of computer technology, signal processing, artificial intelligence, pattern recognition technology; and promote the continuous development of fault diagnosis technology, especially knowledge-based fault diagnosis method has been widely studied. Which, along with the increasingly improved neural network technology, the fault diagnosis method based on neural network has been widespread concern. Since one of the main steps of fault diagnosis is signal processing, while wavelet analysis is an effective tool to process signals and wavelet function has many good characteristics, so the combination of wavelet and neural network, so called wavelet neural network, has become a focus in fault diagnosis field recently.

Methods to Improve the Accuracy of Urease Respiratory Diagnosis of Helicobacter Pylori

2015 ◽  
Vol 22 (3) ◽  
pp. 86-92
Author(s):  
Ясовеев ◽  
V. Yasoveev ◽  
Матанцев ◽  
A. Matantsev ◽  
Уразбахтина ◽  
...  
Keyword(s):  
Neural Network ◽  
Gas Sensors ◽  
Gas Mixture ◽  
Network Technology ◽  
Electronic Database ◽  
Cross Sensitivity ◽  
Diagnosis Method ◽  
H Pylori ◽  
Mixed Gases ◽  
Neural Network Technology

This article describes existing methods of H. pylori diseases diagnosis, procedures of interpreting acquired results and the diagnosis method with the use of semi-conductor catalytic gas sensors combined into the system. Gas sensors have cross-sensitivity to various gases in addition for which they are designed. The use of several sensors allows to reduce the influence of mixed gases. This method is especially useful within medical entities, where air inside can contain alcohol or chloramine vapors. Sensors are selected in the way to overlap main sensor´s cross-sensitivity zone to the maximum extent possible. This is how mixed gases´ influence on the main sensor is compensated. The proposed system uses methods of artificial neural network technology, which allows to enhance system´s stability in changing gas mixture. Due to microcontroller driven calculations, the system can automatically provide data processing. The proposed system can reduce the influence of factors that contribute uncertainty to the measurement result. These results can be transmitted to PC, which one can use to create electronic database or to hold case history.

Application of Ant Colony Algorithms and Neural Networks in Fault Diagnosis

2014 ◽  
Vol 556-562 ◽  
pp. 3014-3017
Author(s):  
Jing Bo Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Ant Colony Algorithms ◽  
Weak Points ◽  
Diagnosis Method ◽  
Computational Simplicity ◽  
Minimum Points ◽  
Neural Network Technology

Neural network technology is widely applied due to its computational simplicity and versatility. But, this method has some weak points, for example, slow convergence, less accurate and easy to fall into local minimum points. Combined ant colony algorithm and neural network for fault diagnosis, it can overcome the limitations of a single fault diagnosis method. Ant colony neural network method is applied to gearbox fault diagnosis, the results show that the diagnosis with characteristics of high precision, strong scientific and practical wider.

The Research of Armored Vehicles Weapon System Fault Diagnosis Method

2014 ◽  
Vol 678 ◽  
pp. 309-312
Author(s):  
Hai Feng Xu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Tree ◽  
Original Data ◽  
Tree Model ◽  
Difficult Situation ◽  
The Neural Network ◽  
Diagnosis Method ◽  
Armored Vehicles ◽  
Neural Network Technology

For armored vehicles electrical system fault diagnosis of fault original data collection difficult situation, this paper introduces the fault diagnosis technology based on fault tree model and fault diagnosis based on neural network technology, and the two kinds of fusion technology, complement each other, with a certain type of equipment control system as an example the case analysis, illustrates the fault tree of the neural network and the rationality and validity of the integrated fault diagnosis thinking.

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%.

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