Thermal condition monitoring system using log-polar mapping, quaternion correlation and max-product fuzzy neural network classification

2010 ◽  
Vol 74 (1-3) ◽  
pp. 164-177 ◽  
Author(s):  
Wai-Kit Wong ◽  
Chu-Kiong Loo ◽  
Way-Soong Lim ◽  
Poi-Ngee Tan
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Thermal Condition ◽  
Fuzzy Neural Network ◽  
Neural Network Classification ◽  
Fuzzy Neural ◽  
Condition Monitoring System

scholarly journals Klasifikasi Motif Batik Berbasis Kemiripan Ciri dengan Wavelet Transform dan Fuzzy Neural Network

2014 ◽  
Vol 5 (1) ◽  
pp. 361
Author(s):  
A Haris Rangkuti
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fuzzy Neural Network ◽  
Classification Methods ◽  
Neural Network Classification ◽  
Fuzzy Input ◽  
Fuzzy Neural ◽  
Input Variables

 This paper introduces a classification of the image of the batik process, which is based on the similarity of the characteristics, by combining the method of wavelet transform Daubechies type 2 level 2, to process the characteristic texture consisting of standard deviation, mean and energy as input variables, using the method of Fuzzy Neural Network (FNN). Fuzzyfikasi process will be carried out all input values with five categories: Very Low (VL), Low (L), Medium (M), High (H) and Very High (VH). The result will be a fuzzy input in the process of neural network classification methods. The result will be a fuzzy input in the process of neural network classification methods. For the image to be processed seven types of batik motif is ceplok, kawung, lereng, parang, megamendung, tambal and nitik. The results of the classification process with FNN is rule generation, so for the new image of batik can be immediately known motif types after treatment with FNN classification.  For the degree of precision of this method is 86-92%.

Research of Intelligent Substation Condition Monitoring Based on the Neural Network

2013 ◽  
Vol 325-326 ◽  
pp. 692-696
Author(s):  
Da Peng Chai ◽  
Qiang Qiang Xue ◽  
Ling Mei Wang ◽  
Xing Yong Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Electric Power ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Power Equipment ◽  
The Neural Network ◽  
Condition Monitoring System ◽  
Artificial Neural Network Ann

The substation electric power equipment condition monitoring is the basis of intelligent substation. This paper analyzes the composition of the substation electric power equipment condition monitoring system and monitoring parameters, and with the transformer condition monitoring as an example, this paper proposes fault diagnosis methods of electric power equipment using artificial neural network(ANN).

scholarly journals Development of a condition monitoring system for compressor equipment with neural network data analysis

2019 ◽  
Vol 1399 ◽  
pp. 022058
Author(s):  
D K Zyryanov ◽  
V V Bukhtoyarov ◽  
N A Bukhtoyarova ◽  
V V Kukartsev ◽  
V S Tynchenko ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Network Data ◽  
Condition Monitoring System ◽  
Compressor Equipment

Quaternion Based Machine Condition Monitoring System

2010 ◽  
pp. 476-508
Author(s):  
Wai Kit Wong ◽  
Chu Kiong Loo ◽  
Way Soong Lim
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Thermal Image ◽  
Image Correlation ◽  
Reference Image ◽  
Machine Condition ◽  
Machine Condition Monitoring ◽  
Fuzzy Neural ◽  
Condition Monitoring System ◽  
Complex Ratio

In this chapter, a new and effective quaternion based machine condition monitoring system using log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier is discussed. Two classification characteristics namely: peak to sidelobe ratio (PSR) and real to complex ratio of the discrete quaternion correlation output (?-value) are applied in the quaternion based machine condition monitoring system. Large PSR and ?-value are observed in case of a good match among correlation of the input thermal image with a particular reference image, while small PSR and ?-value are observed in case of a bad/not match among correlation of the input thermal image with a particular reference image. Some simulation results show that log-polar mapping actually help solving rotation and scaling invariant problems in quaternion based thermal image correlation. Log-polar mapping can help in smoothing the output correlation plane, and hence it provides a better way for measuring PSR and ?-values. Results also show that quaternion based machine condition monitoring system is an efficient machine condition monitoring system with accuracy more than 98%.

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